Add Stage 2 audit/Long-CoT LoRA + comprehensive model card

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,132 @@
+---
+license: apache-2.0
+base_model: samscrack/Qwopus3.6-27B-solidity-sft-stage1B
+language:
+  - en
+library_name: peft
+tags:
+  - solidity
+  - smart-contracts
+  - security-audit
+  - long-cot
+  - reasoning
+  - chain-of-thought
+  - lora
+  - qlora
+  - peft
+  - blockchain
+  - ethereum
+  - sft
+datasets:
+  - samscrack/solidity-audit-cot
+pipeline_tag: text-generation
+inference: false
+---
+
+# Qwopus3.6-27B-solidity-audit-stage2
+
+> ⚠️ **Intermediate checkpoint — Stage 2 of 5.** This is the audit / Long-CoT reasoning LoRA, layered on top of the Stage 1B SFT base. **Not yet RFT-tuned** — Stage 3 (rejection-sampling fine-tuning against `forge test`) and the optional Stage 4 (GSPO RL with executor reward) come next.
+
+A LoRA r=16 adapter on top of `Qwopus3.6-27B-solidity-sft1B-merged` (Stage 1B merged into bf16 base) that teaches the model to:
+- read a Solidity contract end-to-end
+- emit a long chain-of-thought (8-15 substantive paragraphs) in `<think>...</think>`
+- output a structured finding (severity, category, location, description, impact, fix, confidence)
+
+## Pipeline context
+
+| # | Stage | Status | Output |
+|---|---|---|---|
+| 0 | Continued pretrain (DoRA on Solidity corpus) | ✅ done | [`Qwopus3.6-27B-solidity-cpt-stageA`](https://huggingface.co/samscrack/Qwopus3.6-27B-solidity-cpt-stageA) |
+| 1 | SFT (instruction): spec → contract | ✅ done | [`Qwopus3.6-27B-solidity-sft-stage1B`](https://huggingface.co/samscrack/Qwopus3.6-27B-solidity-sft-stage1B) |
+| **2** | **SFT (audit / Long-CoT reasoning)** | **✅ done — this repo** | this repo |
+| 3 | RFT (rejection-sampling FT against `forge test`) | ⬜ planned | TBD |
+| 4 | GSPO (sequence-level RL with executor reward) | ⬜ optional | TBD |
+
+## Training data
+
+- **Corpus**: [`samscrack/solidity-audit-cot`](https://huggingface.co/datasets/samscrack/solidity-audit-cot) — Long-CoT audit traces generated by Claude Opus 4.7 (adaptive thinking, xhigh effort) over real Solidity contracts.
+- **Strict-quality cleaned**: 7,707 rows. **8K-friendly subset for this stage**: 6,140 rows (filtered to ≤30,000 chars total → fits in 8K ctx). The remaining 1,567 long-tail rows are preserved at [`audit_cot_clean_long_tailed.jsonl`] for a future longer-context Stage 2 re-run.
+- **After ctx-filter at training time**: 6,106 rows (34 marginal rows dropped).
+- **Severity distribution (full clean corpus)**: 33% high · 25% medium · 14% low · 14% informational · 14% none. The `none` rows are deliberate — they teach the model not to fabricate vulnerabilities.
+
+Each row's user turn is the contract code with an "Audit this contract" instruction. The assistant turn is `<think>{12 reasoning paragraphs joined}</think>` followed by a structured finding rendered as markdown.
+
+## Recipe (Stage 2 specifics, per SOTA-plan §2)
+
+- **LoRA**: r=16, α=16, dropout=0 (smaller than Stage 1B's r=64 — layered on, not replacing)
+- **Target modules**: `q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj, out_proj`
+- **Trainable parameters**: 88,342,528 (~0.60% of effective 14.8B param count)
+- **Quantization**: base loaded in 4-bit (BnB NF4); adapter weights bf16
+- **Effective batch size**: 72 (4 per device × 9 grad accum × 2 GPUs)
+- **Sequence length**: 8,192
+- **Optimizer**: 8-bit AdamW, weight decay 0.001
+- **Learning rate**: 1e-4 (half of Stage 1B's 2e-4 — finer adjustment on top of trained base)
+- **Epochs**: 1
+- **Total steps**: 85
+- **Chat template**: `qwen3-thinking` (with `<think>` filled by real Opus-derived reasoning)
+- **Loss masking**: `train_on_responses_only` (loss only on assistant tokens after `<|im_start|>assistant\n<think>`)
+
+## Training metrics
+
+- **Wall time**: 3h 48m
+- **Train loss**: 1.21 (start) → ~0.99 (min) → 1.026 (final under linear decay)
+- **Hardware**: 2× NVIDIA RTX PRO 6000 Blackwell Workstation Edition (96 GB each)
+- **Distributed**: DDP via `torchrun --nproc-per-node=2`
+- **Framework**: [Unsloth](https://github.com/unslothai/unsloth) 2026.4.7 with TRL 0.22.2
+
+## Usage
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load the Stage-1B-merged base, then layer the Stage 2 LoRA
+base = AutoModelForCausalLM.from_pretrained(
+    "samscrack/Qwopus3.6-27B-solidity-sft-stage1B",  # merge with the CPT base first locally
+    torch_dtype="bfloat16", device_map="auto",
+)
+model = PeftModel.from_pretrained(base, "samscrack/Qwopus3.6-27B-solidity-audit-stage2")
+tokenizer = AutoTokenizer.from_pretrained("samscrack/Qwopus3.6-27B-solidity-audit-stage2")
+
+contract = open("MyContract.sol").read()
+messages = [{"role": "user", "content":
+    "Audit the following Solidity contract for security issues. Identify the most "
+    "impactful vulnerability (or 'none' if the contract is clean), reason step-by-step "
+    "about your analysis, then output a structured finding with severity, category, "
+    "location, description, impact, and a concrete fix.\n\n"
+    f"```solidity\n{contract}\n```"}]
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+out = model.generate(**inputs, max_new_tokens=4096, do_sample=False)
+print(tokenizer.decode(out[0], skip_special_tokens=False))
+```
+
+The output will contain a `<think>` block with multi-paragraph reasoning followed by a markdown audit report.
+
+## Limitations
+
+- **Single-finding output by design.** Each response identifies the one most-impactful issue. Multi-finding audits would require either prompting it to "find all issues" or running it with different temperatures and aggregating.
+- **Synthetic teacher.** Reasoning style and breadth reflect Claude Opus 4.7's idiom. The model has been pushed in that direction; output quality is bounded by Opus's audit ceiling.
+- **No `forge test` validation in the loss.** This stage uses next-token cross-entropy on Opus traces. Stage 3 RFT introduces test-pass as a reward signal — that's where functional correctness gets enforced.
+- **Long contracts truncated.** Training set was filtered to contracts that fit in 8K tokens. Contracts >~7,500 tokens may produce shorter or less coherent reasoning at inference time.
+- **Solidity ≥ 0.7 bias** (inherited from Stage 1B's strict pragma filter).
+- **Findings can be wrong.** Like any synthetic-teacher SFT, this model can produce confident-sounding false positives (flagging non-bugs) or miss real issues. Use as a triage assistant, not a substitute for human audit review.
+
+## Related artifacts
+
+- **Stage 0 CPT**: [`samscrack/Qwopus3.6-27B-solidity-cpt-stageA`](https://huggingface.co/samscrack/Qwopus3.6-27B-solidity-cpt-stageA)
+- **Stage 1 SFT**: [`samscrack/Qwopus3.6-27B-solidity-sft-stage1B`](https://huggingface.co/samscrack/Qwopus3.6-27B-solidity-sft-stage1B)
+- **Training data**: [`samscrack/solidity-audit-cot`](https://huggingface.co/datasets/samscrack/solidity-audit-cot)
+- **Eval set (held-out, must not train on)**: [`samscrack/solidity-eval-2026`](https://huggingface.co/datasets/samscrack/solidity-eval-2026)
+
+## Citation
+
+```bibtex
+@misc{qwopus3-6-27b-solidity-audit-stage2-2026,
+  author       = {samscrack},
+  title        = {Qwopus3.6-27B-solidity-audit-stage2: Stage 2 audit/Long-CoT LoRA},
+  year         = {2026},
+  publisher    = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/samscrack/Qwopus3.6-27B-solidity-audit-stage2}},
+}
+```

adapter_config.json

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+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
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+    "parent_library": "transformers.models.qwen3_5.modeling_qwen3_5",
+    "unsloth_fixed": true
+  },
+  "base_model_name_or_path": "/home/sam/models/Qwopus3.6-27B-solidity-sft1B-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": 16,
+  "lora_bias": false,
+  "lora_dropout": 0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.19.1",
+  "qalora_group_size": 16,
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "up_proj",
+    "o_proj",
+    "down_proj",
+    "out_proj",
+    "k_proj",
+    "gate_proj",
+    "v_proj"
+  ],
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+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

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chat_template.jinja

@@ -0,0 +1,88 @@
+{%- 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
+<think>
+' }}
+{%- endif %}

tokenizer.json

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tokenizer_config.json

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+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "248076": {
+      "content": "<|audio_pad|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "248077": {
+      "content": "<|PAD_TOKEN|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    }
+  }
+}