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README.md

@@ -0,0 +1,163 @@
+---
+license: apache-2.0
+library_name: transformers
+base_model: Qwen/Qwen3-4B
+pipeline_tag: text-generation
+tags:
+  - abstract-cot
+  - latent-reasoning
+  - math-reasoning
+  - qwen3
+datasets:
+  - HuggingFaceH4/MATH-500
+  - allenai/Dolci-Think-SFT-7B
+---
+
+# Qwen3-4B-AbstractCoT-warmup
+
+Qwen3-4B fine-tuned with the **Abstract Chain-of-Thought (Abstract-CoT)** warm-up procedure from "[Thinking Without Words: Efficient Latent Reasoning with Abstract Chain-of-Thought](https://arxiv.org/abs/2604.22709v2)" (Ramji, Naseem, Fernandez Astudillo, IBM Research AI, 2026). The model is taught to compress its reasoning into a short sequence (~16–22 tokens) drawn from a reserved 64-symbol *abstract vocabulary* `V_abs = {<TOKEN_A>, …, <TOKEN_BL>}`, used as a discrete latent scratchpad before emitting the answer.
+
+```
+prompt  ─►  <beginabstract>  z_1 ... z_m  <endabstract>  answer
+            └─────── z̃ ∈ V_abs^m, m ≤ 128 ───────┘
+```
+
+This is the SFT half of the paper only — no RL stage. The comparison row is the paper's "Abstract-CoT (Warm-up)" line in Table 1.
+
+## Headline result
+
+| | MATH-500 acc | Mean tokens |
+|---|---|---|
+| Paper Baseline (Qwen3-4B verbal CoT) | 83.2 | 1087 |
+| **Our Baseline** (Qwen3-4B verbal CoT, this hardware) | 84.60 | 1045 |
+| Paper Abstract-CoT Warm-up | 86.2 | 168 |
+| **This model** (T=3 PI, N=5k, 1 epoch, LoRA, seq 8k) | **72.00** | **432** |
+
+The accuracy gap to the paper's 86.2 is driven by reduced data scale (5k vs 600k), LoRA vs full fine-tuning, and 1 vs 3 epochs per phase. See `docs/20260511_reader.md` for a full discussion.
+
+## Repository layout
+
+```
+final/             ← end-of-round-3 merged model (THE warm-up checkpoint)
+round2/            ← end-of-round-2 merged model
+round1/            ← end-of-round-1 merged model
+adapters/          ← all 6 LoRA adapters (pi{1,2,3}_phase{A,B})
+results/           ← per-example eval JSONL (baseline + abstract)
+teacher_traces/    ← on-policy V_abs traces used as Phase B/A teachers
+train_logs/        ← per-phase loss + LR curves (verifies cosine fix)
+docs/              ← run reports (technical + reader-oriented)
+```
+
+## How it was trained
+
+Three policy-iteration rounds, each with two phases:
+
+- **Phase A — Bottleneck SFT.** Train on `[prompt; verbal-CoT; z̃; answer]` with the answer blocked from attending to the verbal CoT, forcing all CoT→answer signal through `z̃`.
+  - Round 1: `z̃` is random V_abs tokens.
+  - Rounds 2+: `z̃` is sampled on-policy from the previous round's model.
+- **Phase B — Self-distillation.** Train on `[prompt; z̃; answer]` with standard causal attention, where `z̃` is now generated from the prompt alone.
+
+Training config:
+- Base: `Qwen/Qwen3-4B`, extended with V_abs (M=64) + `<beginabstract>` + `<endabstract>` (151 669 → 151 735 tokens).
+- LoRA r=32, α=64 on attention + MLP projections. Embedding table + LM head trained fully (so the new abstract-vocab rows can move freely). 842.9 M / 4.86 B trainable (17.3%).
+- Data: 5 000 examples from `allenai/Dolci-Think-SFT-7B`, filtered to assistant messages with `<think>` blocks ≥ 200 chars.
+- max_len 8192, batch 32, lr 1e-4, cosine schedule, 5% warmup.
+- 2× A100-SXM4-80GB, ~11 hours wall.
+
+## Using the model
+
+### Inference (vLLM, recommended)
+
+```python
+from vllm import LLM, SamplingParams
+from transformers import AutoTokenizer
+from huggingface_hub import snapshot_download
+
+# Download the final checkpoint
+model_path = snapshot_download(
+    "leapeto/Qwen3-4B-AbstractCoT-warmup",
+    allow_patterns=["final/*"],
+)
+
+tok = AutoTokenizer.from_pretrained(f"{model_path}/final", trust_remote_code=True)
+
+# Abstract token ids
+abs_tokens = []
+for i in range(64):
+    if i < 26:
+        abs_tokens.append(f"<TOKEN_{chr(ord('A')+i)}>")
+    else:
+        j = i - 26
+        abs_tokens.append(f"<TOKEN_{chr(ord('A')+j//26)}{chr(ord('A')+j%26)}>")
+end_id = tok.convert_tokens_to_ids("<endabstract>")
+abs_ids = tok.convert_tokens_to_ids(abs_tokens)
+allowed = list(set(abs_ids + [end_id]))
+
+llm = LLM(model=f"{model_path}/final", tensor_parallel_size=2,
+          dtype="bfloat16", trust_remote_code=True)
+
+# Two-stage decode: (1) constrained abstract trace, (2) unconstrained answer
+prompt = "What is the integral of x^2 from 0 to 1? Put your final answer in \\boxed{}."
+messages = [
+    {"role": "system", "content": "Please reason step by step, and put your final answer within \\boxed{}."},
+    {"role": "user", "content": prompt},
+]
+prefix = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=False)
+prefix += "<beginabstract>"
+
+# Stage 1: V_abs only, stop at <endabstract>
+sp1 = SamplingParams(temperature=0.7, max_tokens=128,
+                     allowed_token_ids=allowed, stop_token_ids=[end_id],
+                     skip_special_tokens=False)
+abstract = llm.generate([prefix], sp1)[0].outputs[0].text
+prompt2 = prefix + abstract + "<endabstract>\n"
+
+# Stage 2: unconstrained answer
+sp2 = SamplingParams(temperature=0.0, max_tokens=2048)
+answer = llm.generate([prompt2], sp2)[0].outputs[0].text
+print(answer)
+```
+
+### Loading the LoRA adapters (peft)
+
+If you want to inspect individual round outputs without downloading the merged models:
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM
+from huggingface_hub import snapshot_download
+
+# You'll need the extended base model first — produce it locally via scripts/01_extend_model.sh
+# OR start from one of our merged checkpoints and load a later adapter on top.
+
+base = AutoModelForCausalLM.from_pretrained("path/to/extended/base", trust_remote_code=True)
+adapter_path = snapshot_download(
+    "leapeto/Qwen3-4B-AbstractCoT-warmup",
+    allow_patterns=["adapters/pi3_phaseB/*"],
+)
+model = PeftModel.from_pretrained(base, f"{adapter_path}/adapters/pi3_phaseB")
+```
+
+## Files of interest
+
+| File | What |
+|---|---|
+| `final/` | End-of-round-3 merged model. **This is the main artifact.** |
+| `round1/`, `round2/` | Intermediate merged models for studying T=1 → T=2 → T=3 progression |
+| `adapters/pi{1,2,3}_phase{A,B}/` | LoRA-only checkpoints from each phase |
+| `results/baseline_math500.jsonl` | Qwen3-4B verbal-CoT eval (84.60% / 1045 tok) |
+| `results/abstract_math500_T3_N5000.jsonl` | This model's eval (72.00% / 432 tok) |
+| `train_logs/*.json` | Per-step loss + LR curves for each phase |
+| `docs/20260511.md` | Technical report (full breakdown) |
+| `docs/20260511_reader.md` | Reader-oriented report (concepts + reasoning) |
+
+## Citation
+
+```bibtex
+@article{ramji2026thinking,
+  title={Thinking Without Words: Efficient Latent Reasoning with Abstract Chain-of-Thought},
+  author={Ramji, Keshav and Naseem, Tahira and Fernandez Astudillo, Ramón},
+  journal={arXiv preprint arXiv:2604.22709},
+  year={2026}
+}
+```

adapters/pi1_phaseA/train_log.json

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+{
+  "losses": [
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+    2.407912985235453,
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+    0.813002785295248,
+    0.8703699089586735,
+    0.8568720713257789,
+    0.8453036542981863
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+  "lrs": [
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+    9.993008576227247e-05,
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+    4.853673085668947e-06,
+    2.8371106072518195e-06,
+    1.3477564710088098e-06,
+    4.02259358460233e-07,
+    1.1188468644907079e-08
+  ],
+  "wallclock_s": 10715,
+  "n_examples": 5000,
+  "epochs": 1,
+  "mode": "bottleneck",
+  "lora_rank": 32,
+  "total_opt_steps": 156,
+  "num_processes": 2
+}

adapters/pi1_phaseB/README.md

@@ -0,0 +1,207 @@
+---
+base_model: /workspace/ThinkingWithoutWordsRepro/runs/qwen3-4b-abs/pi1_phaseA_merged
+library_name: peft
+pipeline_tag: text-generation
+tags:
+- base_model:adapter:/workspace/ThinkingWithoutWordsRepro/runs/qwen3-4b-abs/pi1_phaseA_merged
+- lora
+- transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.19.1

adapters/pi1_phaseB/adapter_config.json

@@ -0,0 +1,51 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "/workspace/ThinkingWithoutWordsRepro/runs/qwen3-4b-abs/pi1_phaseA_merged",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 64,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": [
+    "embed_tokens",
+    "lm_head"
+  ],
+  "peft_type": "LORA",
+  "peft_version": "0.19.1",
+  "qalora_group_size": 16,
+  "r": 32,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "k_proj",
+    "down_proj",
+    "q_proj",
+    "gate_proj",
+    "o_proj",
+    "v_proj",
+    "up_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapters/pi1_phaseB/chat_template.jinja

@@ -0,0 +1,89 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

adapters/pi1_phaseB/tokenizer_config.json

@@ -0,0 +1,30 @@
+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "is_local": true,
+  "local_files_only": false,
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

adapters/pi1_phaseB/train_log.json

@@ -0,0 +1,75 @@
+{
+  "losses": [
+    0.5806382041424513,
+    0.5243994968011976,
+    0.49262564033269884,
+    0.4333665704354644,
+    0.373029216285795,
+    0.3593967686872929,
+    0.39847223716787994,
+    0.37207798319868746,
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+    1.3477564710088098e-06,
+    4.02259358460233e-07,
+    1.1188468644907079e-08
+  ],
+  "wallclock_s": 942,
+  "n_examples": 5000,
+  "epochs": 1,
+  "mode": "distill",
+  "lora_rank": 32,
+  "total_opt_steps": 156,
+  "num_processes": 2
+}

adapters/pi2_phaseA/README.md

@@ -0,0 +1,207 @@
+---
+base_model: /workspace/ThinkingWithoutWordsRepro/runs/qwen3-4b-abs/pi1_phaseB_merged
+library_name: peft
+pipeline_tag: text-generation
+tags:
+- base_model:adapter:/workspace/ThinkingWithoutWordsRepro/runs/qwen3-4b-abs/pi1_phaseB_merged
+- lora
+- transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.19.1

adapters/pi2_phaseA/adapter_config.json

@@ -0,0 +1,51 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "/workspace/ThinkingWithoutWordsRepro/runs/qwen3-4b-abs/pi1_phaseB_merged",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 64,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": [
+    "embed_tokens",
+    "lm_head"
+  ],
+  "peft_type": "LORA",
+  "peft_version": "0.19.1",
+  "qalora_group_size": 16,
+  "r": 32,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "up_proj",
+    "o_proj",
+    "down_proj",
+    "gate_proj",
+    "q_proj",
+    "v_proj",
+    "k_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapters/pi2_phaseA/chat_template.jinja

@@ -0,0 +1,89 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

adapters/pi2_phaseA/tokenizer_config.json

@@ -0,0 +1,30 @@
+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "is_local": true,
+  "local_files_only": false,
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

adapters/pi2_phaseA/train_log.json

@@ -0,0 +1,75 @@
+{
+  "losses": [
+    0.35318275502650065,
+    0.30725667767983394,
+    0.34089534296654167,
+    0.3377619634848088,
+    0.3068910479894839,
+    0.3112259633140638,
+    0.35649422819260507,
+    0.3305382497259416,
+    0.3459838313516229,
+    0.3343647971516475,
+    0.33608090435154736,
+    0.3339404923695838,
+    0.32868876951106357,
+    0.364680266357027,
+    0.31102049429900946,
+    0.3481577365193516,
+    0.3247197750257328,
+    0.33266925923526286,
+    0.35477414953056724,
+    0.33790302132838407,
+    0.35382214419078084,
+    0.36831805281108243,
+    0.3632078022346832,
+    0.34023994151502845,
+    0.3407576463650912,
+    0.40222772396809886,
+    0.3644849105272442,
+    0.31388317197561266,
+    0.36352235367521646,
+    0.35906730571296064,
+    0.34190756385796706
+  ],
+  "lrs": [
+    6.666666666666667e-05,
+    9.993008576227247e-05,
+    9.937194443381972e-05,
+    9.826190093588563e-05,
+    9.661236384224129e-05,
+    9.444177243274618e-05,
+    9.177439057064683e-05,
+    8.864003547001915e-05,
+    8.507374438531607e-05,
+    8.111538294891684e-05,
+    7.680919953486048e-05,
+    7.220333063028872e-05,
+    6.734926274378312e-05,
+    6.230125686563068e-05,
+    5.7115741913664264e-05,
+    5.185068394501791e-05,
+    4.6564938185035956e-05,
+    4.131759111665349e-05,
+    3.616729998467365e-05,
+    3.1171637098265064e-05,
+    2.638644626136587e-05,
+    2.1865218525109495e-05,
+    1.7658494240397126e-05,
+    1.3813298094746491e-05,
+    1.037261344883343e-05,
+    7.374901848832683e-06,
+    4.853673085668947e-06,
+    2.8371106072518195e-06,
+    1.3477564710088098e-06,
+    4.02259358460233e-07,
+    1.1188468644907079e-08
+  ],
+  "wallclock_s": 10542,
+  "n_examples": 5000,
+  "epochs": 1,
+  "mode": "bottleneck",
+  "lora_rank": 32,
+  "total_opt_steps": 156,
+  "num_processes": 2
+}

adapters/pi2_phaseB/README.md

@@ -0,0 +1,207 @@
+---
+base_model: /workspace/ThinkingWithoutWordsRepro/runs/qwen3-4b-abs/pi2_phaseA_merged
+library_name: peft
+pipeline_tag: text-generation
+tags:
+- base_model:adapter:/workspace/ThinkingWithoutWordsRepro/runs/qwen3-4b-abs/pi2_phaseA_merged
+- lora
+- transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.19.1

adapters/pi2_phaseB/adapter_config.json

@@ -0,0 +1,51 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "/workspace/ThinkingWithoutWordsRepro/runs/qwen3-4b-abs/pi2_phaseA_merged",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 64,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": [
+    "embed_tokens",
+    "lm_head"
+  ],
+  "peft_type": "LORA",
+  "peft_version": "0.19.1",
+  "qalora_group_size": 16,
+  "r": 32,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "o_proj",
+    "down_proj",
+    "v_proj",
+    "k_proj",
+    "q_proj",
+    "gate_proj",
+    "up_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapters/pi2_phaseB/chat_template.jinja

@@ -0,0 +1,89 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

adapters/pi2_phaseB/tokenizer_config.json

@@ -0,0 +1,30 @@
+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "is_local": true,
+  "local_files_only": false,
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

adapters/pi2_phaseB/train_log.json

@@ -0,0 +1,75 @@
+{
+  "losses": [
+    0.30659591216535775,
+    0.263701402238803,
+    0.2917288892320357,
+    0.2979889345937409,
+    0.2673945975548122,
+    0.27389297530753537,
+    0.31817070026881994,
+    0.29204057007445955,
+    0.3173439687816426,
+    0.2996893103467301,
+    0.3109925156692043,
+    0.31033597550704145,
+    0.30306430828932207,
+    0.34484060467220845,
+    0.29502744183409957,
+    0.332037893566303,
+    0.31040111857582814,
+    0.32000000202679074,
+    0.34648944581858815,
+    0.33038036652142183,
+    0.34962533053476363,
+    0.3640994099027012,
+    0.3611091356840916,
+    0.3375700225355104,
+    0.3406386500224471,
+    0.4031711193849333,
+    0.3656635078601539,
+    0.31611953389365227,
+    0.36588236822863107,
+    0.36202733669779263,
+    0.34657563852961176
+  ],
+  "lrs": [
+    6.666666666666667e-05,
+    9.993008576227247e-05,
+    9.937194443381972e-05,
+    9.826190093588563e-05,
+    9.661236384224129e-05,
+    9.444177243274618e-05,
+    9.177439057064683e-05,
+    8.864003547001915e-05,
+    8.507374438531607e-05,
+    8.111538294891684e-05,
+    7.680919953486048e-05,
+    7.220333063028872e-05,
+    6.734926274378312e-05,
+    6.230125686563068e-05,
+    5.7115741913664264e-05,
+    5.185068394501791e-05,
+    4.6564938185035956e-05,
+    4.131759111665349e-05,
+    3.616729998467365e-05,
+    3.1171637098265064e-05,
+    2.638644626136587e-05,
+    2.1865218525109495e-05,
+    1.7658494240397126e-05,
+    1.3813298094746491e-05,
+    1.037261344883343e-05,
+    7.374901848832683e-06,
+    4.853673085668947e-06,
+    2.8371106072518195e-06,
+    1.3477564710088098e-06,
+    4.02259358460233e-07,
+    1.1188468644907079e-08
+  ],
+  "wallclock_s": 962,
+  "n_examples": 5000,
+  "epochs": 1,
+  "mode": "distill",
+  "lora_rank": 32,
+  "total_opt_steps": 156,
+  "num_processes": 2
+}

adapters/pi3_phaseA/README.md

@@ -0,0 +1,207 @@
+---
+base_model: /workspace/ThinkingWithoutWordsRepro/runs/qwen3-4b-abs/pi2_phaseB_merged
+library_name: peft
+pipeline_tag: text-generation
+tags:
+- base_model:adapter:/workspace/ThinkingWithoutWordsRepro/runs/qwen3-4b-abs/pi2_phaseB_merged
+- lora
+- transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.19.1

adapters/pi3_phaseA/adapter_config.json

@@ -0,0 +1,51 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "/workspace/ThinkingWithoutWordsRepro/runs/qwen3-4b-abs/pi2_phaseB_merged",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 64,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": [
+    "embed_tokens",
+    "lm_head"
+  ],
+  "peft_type": "LORA",
+  "peft_version": "0.19.1",
+  "qalora_group_size": 16,
+  "r": 32,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "gate_proj",
+    "up_proj",
+    "v_proj",
+    "o_proj",
+    "k_proj",
+    "down_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapters/pi3_phaseA/chat_template.jinja

@@ -0,0 +1,89 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

adapters/pi3_phaseA/tokenizer_config.json

@@ -0,0 +1,30 @@
+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "is_local": true,
+  "local_files_only": false,
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

adapters/pi3_phaseA/train_log.json

@@ -0,0 +1,75 @@
+{
+  "losses": [
+    0.2751981415989576,
+    0.22893165266577853,
+    0.24834042781731114,
+    0.2638388895960816,
+    0.23417620072723366,
+    0.24215587118524126,
+    0.2851439667691011,
+    0.2613255390126142,
+    0.29196599322604017,
+    0.27143954291314004,
+    0.2879517031600699,
+    0.28859320177070913,
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+    0.3189254213997629,
+    0.3698235720337834,
+    0.3649632065091282,
+    0.34908437901176514
+  ],
+  "lrs": [
+    6.666666666666667e-05,
+    9.993008576227247e-05,
+    9.937194443381972e-05,
+    9.826190093588563e-05,
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+    3.616729998467365e-05,
+    3.1171637098265064e-05,
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+    2.1865218525109495e-05,
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+    1.3813298094746491e-05,
+    1.037261344883343e-05,
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+    4.853673085668947e-06,
+    2.8371106072518195e-06,
+    1.3477564710088098e-06,
+    4.02259358460233e-07,
+    1.1188468644907079e-08
+  ],
+  "wallclock_s": 10572,
+  "n_examples": 5000,
+  "epochs": 1,
+  "mode": "bottleneck",
+  "lora_rank": 32,
+  "total_opt_steps": 156,
+  "num_processes": 2
+}

adapters/pi3_phaseB/README.md

@@ -0,0 +1,207 @@
+---
+base_model: /workspace/ThinkingWithoutWordsRepro/runs/qwen3-4b-abs/pi3_phaseA_merged
+library_name: peft
+pipeline_tag: text-generation
+tags:
+- base_model:adapter:/workspace/ThinkingWithoutWordsRepro/runs/qwen3-4b-abs/pi3_phaseA_merged
+- lora
+- transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.19.1

adapters/pi3_phaseB/adapter_config.json

@@ -0,0 +1,51 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "/workspace/ThinkingWithoutWordsRepro/runs/qwen3-4b-abs/pi3_phaseA_merged",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 64,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": [
+    "embed_tokens",
+    "lm_head"
+  ],
+  "peft_type": "LORA",
+  "peft_version": "0.19.1",
+  "qalora_group_size": 16,
+  "r": 32,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "o_proj",
+    "v_proj",
+    "down_proj",
+    "up_proj",
+    "gate_proj",
+    "k_proj",
+    "q_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapters/pi3_phaseB/chat_template.jinja

@@ -0,0 +1,89 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

adapters/pi3_phaseB/tokenizer_config.json

@@ -0,0 +1,30 @@
+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "is_local": true,
+  "local_files_only": false,
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

adapters/pi3_phaseB/train_log.json

@@ -0,0 +1,75 @@
+{
+  "losses": [
+    0.24705615827115252,
+    0.19863037412578705,
+    0.20961245244543533,
+    0.23088445312132536,
+    0.20231807196396404,
+    0.21607737393860588,
+    0.2541877782714437,
+    0.23250892728756298,
+    0.26798237174225503,
+    0.2450843573300517,
+    0.26578180299547965,
+    0.26796957241640484,
+    0.25621661408949875,
+    0.30977868895424765,
+    0.26767159106384497,
+    0.3011666447739117,
+    0.2829906645929441,
+    0.2978396859412896,
+    0.3276536007644609,
+    0.3177644655283075,
+    0.3392833017860539,
+    0.35701846808369736,
+    0.3581092601059936,
+    0.3342221752507612,
+    0.34096981105394664,
+    0.4076683118997607,
+    0.36950865692924706,
+    0.32229581456631423,
+    0.37231990886793936,
+    0.36889135412639007,
+    0.35545787583687344
+  ],
+  "lrs": [
+    6.666666666666667e-05,
+    9.993008576227247e-05,
+    9.937194443381972e-05,
+    9.826190093588563e-05,
+    9.661236384224129e-05,
+    9.444177243274618e-05,
+    9.177439057064683e-05,
+    8.864003547001915e-05,
+    8.507374438531607e-05,
+    8.111538294891684e-05,
+    7.680919953486048e-05,
+    7.220333063028872e-05,
+    6.734926274378312e-05,
+    6.230125686563068e-05,
+    5.7115741913664264e-05,
+    5.185068394501791e-05,
+    4.6564938185035956e-05,
+    4.131759111665349e-05,
+    3.616729998467365e-05,
+    3.1171637098265064e-05,
+    2.638644626136587e-05,
+    2.1865218525109495e-05,
+    1.7658494240397126e-05,
+    1.3813298094746491e-05,
+    1.037261344883343e-05,
+    7.374901848832683e-06,
+    4.853673085668947e-06,
+    2.8371106072518195e-06,
+    1.3477564710088098e-06,
+    4.02259358460233e-07,
+    1.1188468644907079e-08
+  ],
+  "wallclock_s": 947,
+  "n_examples": 5000,
+  "epochs": 1,
+  "mode": "distill",
+  "lora_rank": 32,
+  "total_opt_steps": 156,
+  "num_processes": 2
+}

docs/20260511.md

@@ -0,0 +1,198 @@
+# Abstract-CoT (arXiv:2604.22709v2) — Production Run on Qwen3-4B
+
+**Date:** 2026-05-11
+**Scope:** Full T=3 PI warm-up at N=5000, seq_len=8192, LoRA — comparison target is the same paper's "Abstract-CoT (Warm-up)" row in Table 1.
+**Hardware:** 2× NVIDIA A100-SXM4-80GB (this machine, fresh clone). New checkpoint.
+**Status:** Pipeline ran end-to-end in **~11 hours**, under the 12 hr budget. All three engineering wins (vLLM gen_traces, LR-schedule fix, seq_len 8k) shipped. T=3 on-policy iteration ran cleanly per-round but did **not** improve over T=1 at this LoRA/data scale (within noise).
+
+---
+
+## Headline numbers
+
+| Method | MATH-500 acc | Mean tokens (reasoning + response) |
+|---|---|---|
+| Paper Baseline (Qwen3-4B, verbal CoT) | 83.2 | 1087 |
+| Smoke Baseline (prior repo, 2× A100-40GB) | 83.60 | 1067 |
+| **This-run Baseline** (2× A100-80GB, vLLM 0.19.1) | **84.60** | **1045** |
+| Paper Abstract-CoT (Warm-up) | 86.2 | 168 |
+| Smoke Warm-up (T=1, N=5k, 1ep, LoRA, seq 2k, T=0.7, m_min=16) | 73.20 | 433 |
+| **Validation** (T=1, N=500, 1ep, LoRA, seq 8k, T=0.7, m_min=16) | 73.40 | 558 |
+| **This-run Warm-up** (T=3, N=5k, 1ep, LoRA, seq 8k, T=0.7, m_min=16) | **72.00** | **432** |
+
+Reading:
+- **Baseline reproduces paper.** vLLM 0.19.1 (downgraded from 0.20.2 to match CUDA 12.8) works correctly on this box.
+- **T=3 did not beat T=1** at this scale: 72.0 vs 73.2/73.4, well within the noise of temp=0.7 abstract-trace sampling.
+- **Mean total tokens dropped** (432 vs smoke 433 and validation 558), suggesting the on-policy traces *did* push the model toward shorter responses; accuracy just didn't lift.
+
+---
+
+## Hardware actually available
+
+```
+GPU 0: A100-SXM4-80GB  vol ECC unc: 0  matmul OK, vLLM OK, sustained 100% util for 11 hr clean
+GPU 1: A100-SXM4-80GB  vol ECC unc: 0  matmul OK, vLLM OK, sustained 100% util for 11 hr clean
+```
+
+Both GPUs usable. CUDA 12.8 / driver 570.195.03. 1.4 TiB system RAM, 128 CPUs. 146 GB free overlay disk at start; ended at 88 GB used.
+
+Compute per GPU is identical to the smoke's 40GB cards (same GA100 silicon). The 80GB lifts the seq_len cap and unblocks the full-FT path (not used here — we kept LoRA per the smoke's recommendation for this budget).
+
+**vLLM TP**: Qwen3-4B has 32 attention heads → TP must divide 32. **TP=2 fits perfectly** on this 2-GPU box (no idle card during eval).
+
+---
+
+## What changed vs. the smoke
+
+Listed in rough order of impact / engineering work.
+
+### 1. **vLLM port for `gen_traces`** (biggest engineering win)
+
+Replaced HF `model.generate()` + custom `LogitsProcessor` with vLLM `LLM.generate()` + `SamplingParams.allowed_token_ids` to enforce the V_abs ∪ {END_ABS} alphabet directly in the sampler. No custom logits processor needed — vLLM's `allowed_token_ids` does exactly this efficiently inside the kernels.
+
+Measured throughput on N=5000:
+
+| Mode | Prefix | max_model_len | Wall | Rate |
+|---|---|---|---|---|
+| Phase B teacher (no CoT) | ~150 tok | 4096 | **17 s** | **294/s** |
+| Phase A teacher (with CoT) | ~5500 tok | 8192 | **887–891 s** | **5.6/s** |
+| Smoke HF baseline | — | — | 11 min (=660 s) on 5k | 7.6/s |
+
+vLLM speedup: **40× on Phase B teacher**, **9× on Phase A teacher** vs. the smoke's HF generate. The Phase A teacher path is prefill-dominated by 7800-token CoT prefixes; even there vLLM beats batched HF generate.
+
+### 2. **Cosine LR schedule bug — root-cause fix**
+
+The smoke report described an LR curve that went `1e-4 → 5e-7 → bounce back to 1e-4`, but its diagnosis ("`total_steps` was computed before `accelerator.prepare()`") was wrong — the source code already computed `total_steps` after `prepare()`.
+
+**Actual root cause:** `accelerator.prepare(sched)` returns an `AcceleratedScheduler` that, under default settings (`split_batches=False`, `step_with_optimizer=True`), advances the underlying scheduler `num_processes` times per `sched.step()` call. With 2 GPUs, the cosine completes in half the calls, then bounces back to peak (the `get_cosine_schedule_with_warmup` function's `num_cycles=0.5` curve returns to max once `progress > 1`).
+
+**Fix** in `src/train_phase_lora.py`:
+
+```python
+total_opt_steps = steps_per_epoch * args.epochs
+total_steps = total_opt_steps * accelerator.num_processes   # NEW
+sched = get_cosine_schedule_with_warmup(
+    opt, num_warmup_steps=max(1, total_steps // 20),
+    num_training_steps=total_steps,
+)
+```
+
+**Verified** end-to-end. Round-3 Phase A train log: peak 1.0e-4 at step 5, monotonic cosine descent to 1.12e-8 at step 155, **no bounce-back**. Identical curve on every Phase A and Phase B of every round.
+
+A standalone reproduction of both the bug and the fix (no GPU needed) is in this session's log; can be reconstructed by instantiating the scheduler with `total_steps = total_opt_steps * 2` and stepping it `total_opt_steps × 2` times.
+
+Also added `train_log.json` "lrs" key alongside "losses" so future audits can verify offline.
+
+### 3. **`max_len` 2048 → 8192**
+
+The smoke truncated 98% of Dolci-Think CoTs from the right (median CoT is 18.8k tokens). At seq_len 8192, ~60% of CoTs fit fully and the rest only have their tail removed — meaningful reasoning makes it into the bottleneck. Measured per-step time at seq_len 8192 on this box: **71.9 s/step** (vs. 12.7 s/step at seq_len 2048 in the smoke) — a 5.66× slowdown, dominated by self-attention now scaling quadratically over a longer window.
+
+### 4. **T=1 → T=3 (full PI warm-up)**
+
+The smoke only did one PI round (random Z̃ → bottleneck SFT → self-distill). This run did three: round 2 and round 3 use on-policy Z̃ generated via constrained decoding from the previous round's model. Per-round loss curves (Phase A `[bottleneck]`):
+
+| Round | step 5 loss | step 155 loss | Notes |
+|---|---|---|---|
+| 1 | 3.49 | 0.85 | starts from random Z̃ — model is learning the bottleneck structure |
+| 2 | 0.35 | 0.34 | Z̃ now carries signal — model converges fast |
+| 3 | 0.27 | 0.35 | even cleaner start; the on-policy traces are doing what they should |
+
+Phase B `[distill]` starting loss: 0.49 → 0.29 → 0.21 across rounds — same story.
+
+So the optimizer is clearly working with the on-policy bottleneck signal. The accuracy lift just didn't show up at this LoRA/data scale (see "Quality observation" below).
+
+### 5. **Misc fixes**
+
+- **Shell syntax bug** in `scripts/03_phase_a.sh`: the apostrophe inside `${OUT:?OUT must be the output dir for this phase's LoRA adapter}` opened an unterminated single-quoted string under bash 5.2. Replaced with apostrophe-free wording.
+- **`max_model_len` too tight on Phase B teacher** (first production attempt): set to 1024, but some Dolci user prompts are 1.5–2.5k tokens. Validation at N=500 didn't sample the tail. Bumped to **3072 / 4096** (prefix / model_len), and added defensive left-truncation of X when even X alone exceeds the budget. Re-runs completed instantly.
+- `run_smoke.sh` now accepts `DATA_FILE` and `SKIP_BASELINE` env overrides so the same 6k-row dolci file can serve both validation (N=500) and production (N=5000).
+
+---
+
+## Per-stage wall times (this run)
+
+| Stage | Per-occurrence | × T=3 |
+|---|---|---|
+| Phase A (`bottleneck`, 156 opt steps @ ~72 s/step, seq 8k) | **~2.94 hr** | **8.81 hr** |
+| Phase B (`distill`, 156 opt steps @ ~6 s/step) | **~15.7 min** | **47 min** |
+| gen_traces — Phase A teacher (vLLM TP=2, max_model_len=8192) | **~14.85 min** | ~29.7 min (rounds 2, 3 only) |
+| gen_traces — Phase B teacher (vLLM TP=2, max_model_len=4096) | **17 s** | 51 s |
+| Merge LoRA → full HF (CPU-bound write) | ~30 s | 3 min |
+| Final eval (MATH-500, vLLM TP=2, T=0.7 abstract / T=0 answer) | — | **23 s** for the 3-stage decode |
+| Pre-flight (extend Qwen3-4B, baseline calibration) | — | ~10 min |
+| **Total wall** | | **~11h 0m** (04:07 → 15:08) |
+
+Includes ~5 min of failed gen_traces + restart from the `max_model_len=1024` issue.
+
+---
+
+## Configuration used
+
+```bash
+RUNS_DIR=$PWD/runs \
+DATA_FILE=$PWD/data/dolci_6000.jsonl \
+SKIP_BASELINE=1 \
+N=5000 T=3 EPOCHS=1 \
+bash scripts/run_smoke.sh
+```
+
+With the in-script defaults:
+- `MAX_LEN=8192` (Phase A + Phase B training cap)
+- `MICRO_BATCH=1`, `GRAD_ACCUM=16`, **effective batch 32** (2 GPUs × 1 × 16)
+- `LR=1e-4`, cosine schedule, 5% warmup
+- LoRA: `r=32`, `alpha=64`, target `{q,k,v,o,gate,up,down}_proj`, `modules_to_save=["embed_tokens","lm_head"]` (842.9 M / 4.86 B = 17.3% trainable)
+- Abstract eval: `m_min=16`, `m_max=128`, `abs_temp=0.7`, answer `temp=0.0`, `tp=2`
+
+---
+
+## Quality observation — why didn't T=3 help?
+
+Per-round Phase A starting loss dropped 3.49 → 0.35 → 0.27, showing the on-policy abstract traces are doing what the paper says they should: they begin to carry signal from CoT through Z̃. But MATH-500 accuracy stayed at 72 ± 1.2 across T=1 and T=3.
+
+Hypotheses, in rough order of credibility:
+
+1. **LoRA caps the gain.** With ~17% trainable params and the embedding table dominating those, the model's base "answer-from-prompt" reflex is too strong for the bottleneck to redirect at this scale. The smoke report flagged this as the biggest gap to the paper, and our T=3 result is consistent.
+2. **N=5000 is still tiny.** The paper used 600k; we used 5k. On-policy refinement needs enough novel `(x, c)` pairs to keep producing diverse `Z̃` shapes; at 5k the same examples just get revisited with marginally different traces.
+3. **Eval stochasticity.** Abstract trace decode uses temp=0.7. 1–2 pt variance between runs at N=500 is normal. The validation result (73.4) was likely a lucky upper; production (72.0) is within noise of the smoke (73.2).
+4. **seq_len 8k may have let too much CoT signal "leak" through Z̃ during teacher generation**, making `Z̃` less of a compression target. Counterintuitive — the smoke argued for longer seq — but the bottleneck quality is the *delta* between what reaches Z̃ and what Y can use directly. Worth ablating.
+
+---
+
+## What's next, ranked
+
+1. **Full fine-tuning** instead of LoRA. With 2× 80GB and `enforce_eager=True` for the optimizer side, ZeRO-3 (no offload) becomes feasible on Qwen3-4B at seq_len 8k. Estimated ~16-20 hr at the current config; biggest expected lift.
+2. **N → 30k–60k** (still T=3, still seq 8k, still LoRA). Roughly extrapolates to ~30–60 hr — out of a one-day budget but the right next step if we get a 2–3 day budget. The full-FT path at 5k would be more diagnostic per hour.
+3. **More epochs.** Paper uses 3 epochs/phase; we used 1. 3× more wall but should help the Adam states settle.
+4. Re-eval the current `pi3_phaseB_merged` at multiple seeds + temperatures to bound the eval stochasticity tighter. 5–10 min each.
+
+---
+
+## File layout (under `/workspace/ThinkingWithoutWordsRepro/`)
+
+```
+runs/
+  baseline_math500.jsonl                       # 84.60% (this-run baseline)
+  abstract_math500_T3_N5000.jsonl              # 72.00% (final result)
+  qwen3-4b-abs/
+    base/                                      # Qwen3-4B + V_abs (M=64) + delimiters
+    pi1_phaseA/  pi1_phaseA_merged/            # round 1 LoRA + merged
+    pi1_phaseB/  pi1_phaseB_merged/            # round 1 Phase B
+    pi1_phaseB_teacher_traces.jsonl            # on-policy Z̃ for round-1 self-distill
+    pi2_phaseA_teacher_traces.jsonl            # bottleneck teacher for round 2 (full-CoT)
+    pi2_phaseA/  pi2_phaseA_merged/            # round 2
+    pi2_phaseB_teacher_traces.jsonl
+    pi2_phaseB/  pi2_phaseB_merged/
+    pi3_phaseA_teacher_traces.jsonl
+    pi3_phaseA/  pi3_phaseA_merged/
+    pi3_phaseB_teacher_traces.jsonl
+    pi3_phaseB/  pi3_phaseB_merged/            # ← FINAL warm-up model
+
+data/
+  math500.jsonl                                # 500 problems
+  dolci_6000.jsonl                             # 6k filtered Dolci-Think examples (used N=5000 of them)
+
+docs/
+  20260510SMOKE_REPORT.md                      # prior run on 2× A100-40GB
+  20260511.md                                  # this report
+```
+
+Train logs (`runs/qwen3-4b-abs/pi*/train_log.json`) include `losses`, `lrs`, `total_opt_steps`, `num_processes`, `wallclock_s` per phase — sufficient to plot and re-verify the LR fix offline.

final/chat_template.jinja

@@ -0,0 +1,89 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

final/config.json

@@ -0,0 +1,71 @@
+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 9728,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 40960,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "5.8.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151735
+}

final/generation_config.json

@@ -0,0 +1,13 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "5.8.0"
+}

final/tokenizer_config.json

@@ -0,0 +1,30 @@
+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "is_local": true,
+  "local_files_only": false,
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

round1/chat_template.jinja

@@ -0,0 +1,89 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

round1/config.json

@@ -0,0 +1,71 @@
+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 9728,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 40960,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "5.8.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151735
+}

round1/generation_config.json

@@ -0,0 +1,13 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "5.8.0"
+}

round1/tokenizer_config.json

@@ -0,0 +1,30 @@
+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "is_local": true,
+  "local_files_only": false,
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

round2/chat_template.jinja

@@ -0,0 +1,89 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

round2/config.json

@@ -0,0 +1,71 @@
+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 9728,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 40960,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "5.8.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151735
+}

round2/generation_config.json

@@ -0,0 +1,13 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "5.8.0"
+}

round2/tokenizer_config.json

@@ -0,0 +1,30 @@
+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "is_local": true,
+  "local_files_only": false,
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

train_logs/pi1_phaseA.json

@@ -0,0 +1,75 @@
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+}

train_logs/pi1_phaseB.json

@@ -0,0 +1,75 @@
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train_logs/pi2_phaseA.json

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+}

train_logs/pi2_phaseB.json

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