Laguna XS.2 upload

.gitattributes

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README.md

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+---
+library_name: vllm
+inference: false
+extra_gated_description: >-
+  To learn more about how we process your personal data, please read our <a
+  href="https://poolside.ai/legal/privacy">Privacy Policy</a>.
+tags:
+- laguna-xs.2
+license: apache-2.0
+pipeline_tag: text-generation
+---
+
+<p align="center">
+  <img alt="poolside-banner" src="https://poolside.ai/assets/laguna/laguna-xs2-banner.svg" width="800px">
+</p>
+
+<p align="center">
+  <a href="https://shimmer.poolside.ai"><strong>Try Laguna XS.2 in Shimmer</strong></a> ·
+  <a href="https://platform.poolside.ai"><strong>Get an API key</strong></a> ·
+  <a href="https://poolside.ai/blog/laguna-a-deeper-dive"><strong>Release blog post</strong></a>
+</p>
+
+<br>
+
+# Laguna XS.2-FP8
+Laguna XS.2-FP8 is a 33B total parameter Mixture-of-Experts model with 3B activated parameters per token designed for agentic coding and long-horizon work on a local machine. It uses Sliding Window Attention with per-head gating in 30 out of 40 layers for fast inference and low KV cache requirements.
+
+> [!NOTE]
+> This is the FP8 variant with an FP8-quantized KV cache. The [BF16](https://huggingface.co/poolside/Laguna-XS.2), [NVFP4](https://huggingface.co/poolside/Laguna-XS.2-NVFP4) and [INT4](https://huggingface.co/poolside/Laguna-XS.2-INT4) variants are also available on Hugging Face.
+
+## Highlights
+- **Mixed SWA and global attention layout**: Laguna XS.2 uses sigmoid gating with per-layer rotary scales, enabling mixed SWA (Sliding Window Attention) and global attention layers in a 3:1 ratio (across 40 total layers)
+- **KV cache in FP8**: KV cache quantized to FP8, reducing memory per token
+- **Native reasoning support**: Interleaved thinking between tool calls with support for enabling and disabling thinking per-request
+- **Local-ready**: At 33B total parameters and 3B activated, Laguna XS.2 is compact enough to run on a Mac with 36 GB of RAM. [Available on Ollama](https://ollama.com/library/laguna-xs.2)
+- **Apache 2.0 license**: Use and modify freely for commercial and non-commercial purposes
+
+---
+
+## Model overview
+
+- Training: pre-training, post-training and reinforcement learning stages
+- Number of parameters: 33B total with 3B activated per token
+- Optimizer: Muon
+- Layers: 40 layers (10 layers with global attention, 30 layers with sliding window attention)
+- Experts: 256 experts with 1 shared expert
+- Sliding Window: 512 tokens
+- Modality: text-to-text
+- Context window: 131,072 tokens
+- Reasoning support: interleaved thinking with preserved thinking
+
+## Benchmark results
+
+<p align="center">
+  <img alt="benchmarks" src="https://poolside.ai/assets/laguna/laguna-xs2-chart.svg" width="800px">
+</p>
+
+| Model                     | Size (total params.) | SWE-bench Verified | SWE-bench Multilingual | SWE-bench Pro (Public Dataset) | Terminal-Bench 2.0 |
+|---------------------------|----------------------|--------------------|------------------------|--------------------------------|--------------------|
+| **Laguna XS.2 (BF16)**    | 33B                  | 68.2%              | 62.4%                  | 44.5%                          | 30.1%              |
+| Devstral Small 2          | 24B dense            | 68.0%              | 55.7%                  | -                              | 22.5%              |
+| Gemma 4 31B IT            | 31B dense            | 52.0%              | 51.7%                  | 35.7%                          | 42.9%              |
+| Qwen3.5-35B-A3B           | 35B                  | 69.2%              | 60.3%                  | 44.6%                          | 40.5%              |
+| Qwen3.6-35B-A3B           | 35B                  | 73.4%              | 67.2%                  | 49.5%                          | 51.5%              |
+| Claude Haiku 4.5          | -                    | 73.3%              | -                      | 39.5%                          | 29.8%              |
+| GPT-5.4 Nano              | -                    | -                  | -                      | 52.4%                          | 46.3%              |
+
+*We used the highest publicly-referenced scores for all comparison models across each benchmark. In almost all cases these were official scores published in release blog posts or equivalent, with the exception of Gemma 4 31B IT where the highest published scores were [reported by the Qwen team](https://qwen.ai/blog?id=qwen3.6-35b-a3b) and Claude Haiku 4.5 where the highest published (verified) scores for SWE-bench Pro and Terminal-Bench 2.0 are from their respective official leaderboards.*
+
+<details>
+<summary>Expand for benchmarking methodology</summary>
+
+All benchmarking for Laguna XS.2 was completed using the Laude Institute’s Harbor Framework with our [agent harness](https://github.com/poolsideai/pool), using a maximum of 500 steps and sandboxed execution using 8 GB RAM/2 CPUs (with the exception of Terminal-Bench 2.0; see below). The same sampling parameters were used for all benchmarking: temperature=0.7 and top_k=20.  Some base task images and verifiers were patched to fix infrastructure reliability issues inherent in task setup, such as rate limits on third-party dependencies in external registries used by the verifier. More details outlining these updates and other findings will follow in a future technical blog post.
+
+- SWE-bench Verified: mean pass@1 averaged over 4 runs.
+- SWE-bench Multilingual: mean pass@1 averaged over 7 runs.
+- SWE-bench Pro: mean pass@1 averaged over 3 runs.
+- Terminal-Bench 2.0: mean pass@1 averaged over 5 runs. 48GB RAM/32 CPUs.
+
+</details>
+
+## Usage
+
+Laguna XS.2 has launch-day support in vLLM and Transformers, and TRT-LLM thanks to the support of the team at NVIDIA.
+
+The fastest way to get started is with our API, directly or using OpenRouter.
+
+> [!NOTE]
+> For complete usage instructions, see the main [Laguna XS.2 model card](https://huggingface.co/poolside/Laguna-XS.2).
+
+### Local deployment
+
+Laguna XS.2 is supported in vLLM and Transformers, and TRT-LLM thanks to the support of the team at NVIDIA. Use Laguna-XS.2 with Ollama (with MLX support) and the mlx-lm framework for the best experience on your local machine.
+
+#### vLLM
+
+The full vLLM recipe is on the main [Laguna XS.2 model card](https://huggingface.co/poolside/Laguna-XS.2). Quantization is detected automatically from `quantization_config` in this checkpoint, so the same command works with `poolside/Laguna-XS.2-FP8` substituted for the model ID. No extra flags required.
+
+> [!NOTE]
+> Please note that, during testing, we discovered that models with FP8-quantised KV caches can produce scrambled output when deployed on non-Hopper GPUs. We are actively investigating this issue with the vLLM team, but in the meantime, you can circumvent this issue by explicitly disabling FP8 KV cache (Laguna XS.2 has 40 layers, so list every layer in `--kv-cache-dtype-skip-layers`):
+>
+> ```shell
+> vllm serve poolside/Laguna-XS.2-FP8 \
+>     --kv-cache-dtype-skip-layers $(seq 0 39) \
+>     --max-model-len 131072 \
+>     --reasoning-parser poolside_v1 \
+>     --tool-call-parser poolside_v1
+> ```
+>
+> The [BF16 checkpoint](https://huggingface.co/poolside/Laguna-XS.2) is unaffected as it does not declare an FP8 KV cache.
+
+#### Transformers
+
+The full Transformers recipe is on the main [Laguna XS.2 model card](https://huggingface.co/poolside/Laguna-XS.2). Substitute `poolside/Laguna-XS.2-FP8` for the model ID; quantization is detected automatically from `quantization_config`.
+
+#### TRT-LLM
+
+> [!NOTE]
+> Requires building TensorRT-LLM from the upstream PR that adds Laguna XS.2 support
+> ([NVIDIA/TensorRT-LLM#13559](https://github.com/NVIDIA/TensorRT-LLM/pull/13559)).
+> Once that PR merges, the same code will work on a released `tensorrt-llm` wheel.
+
+The full TRT-LLM recipe, including the `laguna_minimal_overlay.sh` step needed for `transformers 4.57` compatibility, is on the main [Laguna XS.2 model card](https://huggingface.co/poolside/Laguna-XS.2).
+Quantization is detected automatically from `quantization_config` in this checkpoint, so no extra flags are required:
+
+```python
+from tensorrt_llm import LLM
+
+# OVERLAY built from poolside/Laguna-XS.2-FP8 via laguna_minimal_overlay.sh
+llm = LLM(model=OVERLAY, trust_remote_code=True)
+```
+
+#### Ollama
+
+Visit [Ollama's model library](https://ollama.com/library/laguna-xs.2) to pull to your local machine.
+
+## Controlling reasoning
+
+Laguna XS.2 has native reasoning support and is designed to work best with *preserved thinking*, where `reasoning` content from prior assistant messages is preserved in the message history. This model will generally reason before calling tools and between tool calls.
+
+<details>
+<summary>Expand for example</summary>
+
+```python
+import json
+from openai import OpenAI
+
+client = OpenAI(
+  base_url="https://inference.poolside.ai/v1",
+  api_key="...",
+)
+
+model = "poolside/laguna-xs.2"
+
+tools = [{"type": "function", "function": {
+  "name": "shell",
+  "description": "Execute a bash command and return the output.",
+  "parameters": {"type": "object", "properties": {"cmd": {"type": "string"}}, "required": ["cmd"]},
+}}]
+
+messages = [
+  {"role": "system", "content": "You are a coding agent with access to a shell tool."},
+  {"role": "user", "content": "Run uname -a"},
+]
+
+# Thinking is enabled by default when the server sets --default-chat-template-kwargs {"enable_thinking": True}
+# When using the Poolside API (https://inference.poolside.ai/v1), this flag is set by default
+response = client.chat.completions.create(
+  model=model,
+  messages=messages,
+  tools=tools,
+  stream=True,
+)
+
+reasoning, content, tool_calls = "", "", []
+for chunk in response:
+  delta = chunk.choices[0].delta
+  if hasattr(delta, "reasoning_content") and delta.reasoning_content:
+    reasoning += delta.reasoning_content
+  if hasattr(delta, "content") and delta.content:
+    content += delta.content
+  if hasattr(delta, "tool_calls") and delta.tool_calls:
+    for tc in delta.tool_calls:
+      if tc.index >= len(tool_calls):
+        tool_calls.append({"id": tc.id, "function": {"name": "", "arguments": ""}})
+      if tc.function.name:
+        tool_calls[tc.index]["function"]["name"] = tc.function.name
+      if tc.function.arguments:
+        tool_calls[tc.index]["function"]["arguments"] += tc.function.arguments
+
+print(f"Reasoning: {reasoning}\nContent: {content}\nTool calls: {tool_calls}\n")
+
+# Return reasoning in the next request for best performance
+messages.append({
+  "role": "assistant",
+  "content": content,
+  "reasoning_content": reasoning,
+  "tool_calls": [{"id": tc["id"], "type": "function", "function": tc["function"]} for tc in tool_calls]
+})
+
+messages.append({
+  "role": "tool",
+  "tool_call_id": tool_calls[0]["id"],
+  "content": json.dumps({"stdout": "Darwin arm64", "exit_code": "0"})
+})
+
+response = client.chat.completions.create(
+  model=model,
+  messages=messages,
+  tools=tools,
+  stream=True,
+)
+
+reasoning, content = "", ""
+for chunk in response:
+  delta = chunk.choices[0].delta
+  if hasattr(delta, "reasoning_content") and delta.reasoning_content:
+    reasoning += delta.reasoning_content
+  if hasattr(delta, "content") and delta.content:
+    content += delta.content
+
+print(f"Reasoning: {reasoning}\nContent: {content}")
+```
+
+</details>
+
+### Disabling reasoning
+
+You can disable thinking by setting `enable_thinking` to `False` in a request or by not providing `--default-chat-template-kwargs {"enable_thinking": True}` or equivalent when starting the server.
+
+<details>
+<summary>Expand for example</summary>
+
+```python
+from openai import OpenAI
+client = OpenAI()
+
+completion = client.chat.completions.create(
+  model="poolside/laguna-xs.2",
+  messages=[
+    {"role": "user", "content": "Write a retry wrapper with exponential backoff."}
+  ],
+  extra_body={
+    "chat_template_kwargs": { "enable_thinking": False },
+  },
+  stream=True
+)
+
+for chunk in completion:
+    print(chunk.choices[0].delta)
+```
+
+</details>
+
+For agentic coding use cases, we recommend enabling thinking and preserving reasoning in message history as outlined in the [Controlling reasoning] section.
+
+## License
+
+This model is licensed under the [Apache 2.0 License](https://huggingface.co/poolside/Laguna-XS.2-FP8/blob/main/LICENSE.md).
+
+## Intended and Responsible Use 
+
+Laguna XS.2-FP8 is designed for software engineering and agentic coding use cases, and you are responsible for confirming that it is appropriate for your intended application. Laguna XS.2-FP8 is subject to the [Apache 2.0 License](https://huggingface.co/poolside/Laguna-XS.2-FP8/blob/main/LICENSE.md), and should be used consistently with Poolside's [Acceptable Use Policy](https://poolside.ai/legal/acceptable-use-policy). We advise against circumventing Laguna XS.2-FP8 safety guardrails without implementing substantially equivalent mitigations appropriate for your use case.
+
+Please report security vulnerabilities or safety concerns to [security@poolside.ai](mailto:security@poolside.ai).

chat_template.jinja

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+{#- Iteration on laguna_glm_thinking_v5/chat_template.jinja -#}
+{#- Adds a default system message (used when no system message is provided in `messages`). -#}
+{{- "〈|EOS|〉" -}}
+{%- set enable_thinking = enable_thinking | default(false) -%}
+{%- set render_assistant_messages_raw = render_assistant_messages_raw | default(false) -%}
+{%- set add_generation_prompt = add_generation_prompt | default(false) -%}
+
+{#- ───── header (system message) ───── -#}
+{%- set system_message = "You are a helpful, conversationally-fluent assistant made by Poolside. You are here to be helpful to users through natural language conversations." -%}
+{%- if messages and messages[0].role == "system" -%}
+  {%- set system_message = messages[0].content -%}
+{%- endif -%}
+
+{%- if (system_message and system_message.strip()) or tools -%}
+  {{- "<system>\n" -}}
+
+  {%- if system_message and system_message.strip() -%}
+    {{- "\n" -}}
+    {{- system_message.rstrip() -}}
+  {%- endif -%}
+
+  {%- if tools -%}
+    {{- "\n\n### Tools\n\n" -}}
+    {%- set ns = namespace(tool_string="You may call functions to assist with the user query.\n"
+          ~ "All available function signatures are listed below:\n"
+          ~ "<available_tools>\n") -%}
+    {%- for tool in tools -%}
+      {%- set ns.tool_string = ns.tool_string ~ (tool | tojson) ~ "\n" -%}
+    {%- endfor -%}
+    {%- if enable_thinking -%}
+      {%- set tool_string = ns.tool_string + "</available_tools>\n\n" ~
+            "Wrap your thinking in '<think>', '</think>' tags, followed by a function call. For each function call, return an unescaped XML-like object with function name and arguments within '<tool_call>' and '</tool_call>' tags, like here:\n" ~
+            "<think> your thoughts here </think>\n" ~
+            "<tool_call>function-name\n<arg_key>argument-key</arg_key>\n<arg_value>value-of-argument-key</arg_value>\n" ~
+            "</tool_call>" -%}
+    {%- else -%}
+      {%- set tool_string = ns.tool_string + "</available_tools>\n\n" ~
+            "For each function call, return an unescaped XML-like object " ~
+            "with function name and arguments within '<tool_call>' and '</tool_call>' tags, like here:\n" ~
+            "<tool_call>function-name\n<arg_key>argument-key</arg_key>\n<arg_value>value-of-argument-key</arg_value>\n" ~
+            "</tool_call>" -%}
+    {%- endif -%}
+    {{- tool_string -}}
+  {%- endif -%}
+
+  {{- "\n</system>\n" -}}
+{%- endif -%}
+
+{#- ───── main loop ───── -#}
+{%- for message in messages -%}
+  {%- set content = message.content if message.content is string else "" -%}
+  {%- if message.role == "user" -%}
+    {{- "<user>\n" + content + "\n</user>\n" -}}
+  {%- elif message.role == "assistant" -%}
+    {%- generation -%}
+      {{- "<assistant>\n" -}}
+      {%- if render_assistant_messages_raw -%}
+        {#- Raw mode: prepend the generation prompt token, then dump content verbatim. -#}
+        {#- The generation prompt is <think> when enable_thinking, </think> otherwise. -#}
+        {#- Only prepend if content doesn't already start with it. -#}
+        {%- if enable_thinking -%}
+          {%- if not content.startswith('<think>') -%}
+            {{- '<think>' -}}
+          {%- endif -%}
+        {%- else -%}
+          {%- if not content.startswith('</think>') -%}
+            {{- '</think>' -}}
+          {%- endif -%}
+        {%- endif -%}
+        {{- content -}}
+        {#- Append closing tag if content doesn't already end with it. -#}
+        {%- if not content.endswith('</assistant>\n') and not content.endswith('</assistant>') -%}
+          {{- '\n</assistant>' -}}
+        {%- endif -%}
+        {{- "\n" -}}
+      {%- else -%}
+        {#- Extract reasoning content from message.reasoning (vLLM field name) or message.reasoning_content, or from <think> tags -#}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning is string %}
+          {%- set reasoning_content = message.reasoning %}
+        {%- elif message.reasoning_content is string %}
+          {%- set reasoning_content = message.reasoning_content %}
+        {%- endif %}
+        {#- Always strip <think> tags from content if present to avoid duplication -#}
+        {%- if '</think>' in content %}
+          {%- if not reasoning_content %}
+            {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+          {%- endif %}
+          {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+        {%- endif %}
+        {#- Display reasoning content for all messages -#}
+        {%- if reasoning_content -%}
+          {{- '<think>\n' + reasoning_content.strip() + '\n</think>\n' -}}
+        {%- else -%}
+          {{- '</think>\n' -}}
+        {%- endif -%}
+        {#- Display main content -#}
+        {%- if content.strip() -%}
+          {{- content.strip() ~ "\n" -}}
+        {%- endif -%}
+        {%- if message.tool_calls -%}
+          {%- for tool_call in message.tool_calls -%}
+            {%- set function_data = tool_call.function -%}
+            {{- '<tool_call>' + function_data.name }}
+            {% set _args = function_data.arguments %}
+            {%- for k, v in _args.items() -%}
+              {{- "<arg_key>" ~ k ~ "</arg_key>\n" -}}
+              {{- "<arg_value>"}}{{ v | tojson(ensure_ascii=False) if v is not string else v }}{{ "</arg_value>\n" -}}
+            {%- endfor -%}
+            {{- "</tool_call>\n" -}}
+          {%- endfor -%}
+        {%- endif -%}
+        {{- "</assistant>\n" -}}
+      {%- endif -%}
+    {%- endgeneration -%}
+  {%- elif message.role == "tool" -%}
+    {{- "<tool_response>\n" + content + "\n</tool_response>\n" -}}
+  {%- elif message.role == "system" and loop.index0 != 0 -%}
+    {#- Render additional system messages (skip the first one which is handled separately in the header) -#}
+    {{- "<system>\n" + content + "\n</system>\n" -}}
+  {%- endif -%}
+{%- endfor -%}
+{#- ───── generation prompt ───── -#}
+{%- if add_generation_prompt -%}
+  {{- "<assistant>\n" -}}
+  {#- ───── Include reasoning mode directive ───── -#}
+  {%- if not enable_thinking %}
+    {{- '</think>' -}}
+  {%- else %}
+    {{- '<think>' -}}
+  {%- endif %}
+{%- endif -%}

config.json

@@ -0,0 +1,294 @@
+{
+  "architectures": [
+    "LagunaForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_laguna.LagunaConfig",
+    "AutoModelForCausalLM": "modeling_laguna.LagunaForCausalLM"
+  },
+  "model_type": "laguna",
+  "vocab_size": 100352,
+  "hidden_size": 2048,
+  "intermediate_size": 8192,
+  "num_hidden_layers": 40,
+  "num_attention_heads": 48,
+  "num_key_value_heads": 8,
+  "head_dim": 128,
+  "max_position_embeddings": 131072,
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "rms_norm_eps": 1e-06,
+  "num_experts": 256,
+  "num_experts_per_tok": 8,
+  "moe_intermediate_size": 512,
+  "shared_expert_intermediate_size": 512,
+  "router_aux_loss_coef": 0.0,
+  "bos_token_id": 2,
+  "eos_token_id": [
+    2,
+    24
+  ],
+  "pad_token_id": 9,
+  "tie_word_embeddings": false,
+  "use_cache": true,
+  "torch_dtype": "bfloat16",
+  "gating": true,
+  "sliding_window": 512,
+  "rope_parameters": {
+    "full_attention": {
+      "rope_theta": 500000.0,
+      "rope_type": "yarn",
+      "factor": 32.0,
+      "original_max_position_embeddings": 4096,
+      "beta_slow": 1.0,
+      "beta_fast": 64.0,
+      "attention_factor": 1.0,
+      "partial_rotary_factor": 0.5
+    },
+    "sliding_attention": {
+      "rope_type": "default",
+      "rope_theta": 10000.0,
+      "partial_rotary_factor": 1.0
+    },
+    "original_max_position_embeddings": 4096
+  },
+  "layer_types": [
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention"
+  ],
+  "moe_apply_router_weight_on_input": false,
+  "partial_rotary_factor": 0.5,
+  "mlp_layer_types": [
+    "dense",
+    "sparse",
+    "sparse",
+    "sparse",
+    "sparse",
+    "sparse",
+    "sparse",
+    "sparse",
+    "sparse",
+    "sparse",
+    "sparse",
+    "sparse",
+    "sparse",
+    "sparse",
+    "sparse",
+    "sparse",
+    "sparse",
+    "sparse",
+    "sparse",
+    "sparse",
+    "sparse",
+    "sparse",
+    "sparse",
+    "sparse",
+    "sparse",
+    "sparse",
+    "sparse",
+    "sparse",
+    "sparse",
+    "sparse",
+    "sparse",
+    "sparse",
+    "sparse",
+    "sparse",
+    "sparse",
+    "sparse",
+    "sparse",
+    "sparse",
+    "sparse",
+    "sparse"
+  ],
+  "use_bidirectional_attention": false,
+  "moe_routed_scaling_factor": 2.5,
+  "num_attention_heads_per_layer": [
+    48,
+    64,
+    64,
+    64,
+    48,
+    64,
+    64,
+    64,
+    48,
+    64,
+    64,
+    64,
+    48,
+    64,
+    64,
+    64,
+    48,
+    64,
+    64,
+    64,
+    48,
+    64,
+    64,
+    64,
+    48,
+    64,
+    64,
+    64,
+    48,
+    64,
+    64,
+    64,
+    48,
+    64,
+    64,
+    64,
+    48,
+    64,
+    64,
+    64
+  ],
+  "quantization_config": {
+    "config_groups": {
+      "group_0": {
+        "format": "float-quantized",
+        "input_activations": {
+          "actorder": null,
+          "block_structure": null,
+          "dynamic": true,
+          "group_size": 128,
+          "num_bits": 8,
+          "observer": null,
+          "observer_kwargs": {},
+          "scale_dtype": null,
+          "strategy": "group",
+          "symmetric": true,
+          "type": "float",
+          "zp_dtype": null
+        },
+        "output_activations": null,
+        "targets": [
+          "Linear"
+        ],
+        "weights": {
+          "actorder": null,
+          "block_structure": [
+            128,
+            128
+          ],
+          "dynamic": false,
+          "group_size": null,
+          "num_bits": 8,
+          "observer": "memoryless_minmax",
+          "observer_kwargs": {},
+          "scale_dtype": null,
+          "strategy": "block",
+          "symmetric": true,
+          "type": "float",
+          "zp_dtype": null
+        }
+      }
+    },
+    "format": "float-quantized",
+    "global_compression_ratio": null,
+    "ignore": [
+      "lm_head",
+      "re:.*\\.self_attn\\.q_proj$",
+      "re:.*\\.self_attn\\.k_proj$",
+      "re:.*\\.self_attn\\.v_proj$",
+      "re:.*\\.self_attn\\.o_proj$",
+      "re:.*\\.self_attn\\.g_proj$",
+      "re:.*\\.mlp\\.gate$"
+    ],
+    "kv_cache_scheme": {
+      "actorder": null,
+      "block_structure": null,
+      "dynamic": false,
+      "group_size": null,
+      "num_bits": 8,
+      "observer": "minmax",
+      "observer_kwargs": {},
+      "scale_dtype": null,
+      "strategy": "tensor",
+      "symmetric": true,
+      "type": "float",
+      "zp_dtype": null
+    },
+    "quant_method": "compressed-tensors",
+    "quantization_status": "compressed",
+    "sparsity_config": {},
+    "transform_config": {
+      "config_groups": {
+        "R1": {
+          "apply": [
+            {
+              "ignore": [],
+              "inverse": false,
+              "location": "weight_output",
+              "targets": [
+                "re:.*embed_tokens$",
+                "re:.*o_proj$",
+                "re:.*down_proj$"
+              ]
+            },
+            {
+              "ignore": [],
+              "inverse": true,
+              "location": "weight_input",
+              "targets": [
+                "re:.*q_proj$",
+                "re:.*k_proj$",
+                "re:.*v_proj$",
+                "re:.*gate_proj$",
+                "re:.*up_proj$",
+                "re:.*mlp.gate$",
+                "re:.*g_proj$",
+                "re:.*lm_head$"
+              ]
+            }
+          ],
+          "head_dim": 128,
+          "precision": "torch.float64",
+          "randomize": false,
+          "requires_grad": false,
+          "type": "hadamard"
+        }
+      }
+    },
+    "version": "0.14.1.dev11+gf2ee47b"
+  }
+}

configuration_laguna.py

@@ -0,0 +1,213 @@
+# Copyright 2026 Poolside and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import Any, Literal
+
+from huggingface_hub.dataclasses import strict
+
+from transformers.configuration_utils import PreTrainedConfig
+from transformers.modeling_rope_utils import RopeParameters
+from transformers.utils import auto_docstring
+
+
+@auto_docstring(checkpoint="poolside/laguna-XS.2")
+@strict
+class LagunaConfig(PreTrainedConfig):
+    r"""
+    partial_rotary_factor (`float`, *optional*):
+        Fraction of ``head_dim`` to rotate. Folded into each ``rope_parameters[layer_type]``
+        entry by ``__post_init__``.
+    num_attention_heads_per_layer (`list[int]`, *optional*):
+        Per-layer override for ``num_attention_heads``. Length must equal ``num_hidden_layers``.
+    mlp_layer_types (`list[str]`, *optional*):
+        Per-layer MLP type — ``"dense"`` or ``"sparse"``. Length must equal
+        ``num_hidden_layers``. Defaults to first layer dense, rest sparse.
+    moe_routed_scaling_factor (`float`, *optional*, defaults to 1.0):
+        Scalar applied to routed-expert output before combining with the shared-expert output.
+    moe_apply_router_weight_on_input (`bool`, *optional*, defaults to `False`):
+        Whether to apply router weights to the MoE input rather than the output. Not supported
+        in transformers yet; ``True`` will raise a ``NotImplementedError`` for now.
+    moe_router_logit_softcapping (`float`, *optional*, defaults to 0.0):
+        Scaling factor when applying tanh softcapping on the logits of the MoE router logits.
+
+    Example:
+
+    ```python
+    >>> from transformers import LagunaModel, LagunaConfig
+
+    >>> configuration = LagunaConfig()
+    >>> model = LagunaModel(configuration)
+    >>> configuration = model.config
+    ```
+    """
+
+    model_type = "laguna"
+    keys_to_ignore_at_inference = ["past_key_values"]
+    base_model_tp_plan = {
+        "layers.*.self_attn.q_proj": "colwise",
+        "layers.*.self_attn.k_proj": "colwise",
+        "layers.*.self_attn.v_proj": "colwise",
+        "layers.*.self_attn.g_proj": "colwise",
+        "layers.*.self_attn.o_proj": "rowwise",
+        "layers.*.self_attn.q_norm": "replicated_with_grad_allreduce",
+        "layers.*.self_attn.k_norm": "replicated_with_grad_allreduce",
+        "layers.*.mlp.gate_proj": "colwise",
+        "layers.*.mlp.up_proj": "colwise",
+        "layers.*.mlp.down_proj": "rowwise",
+        "layers.*.mlp.experts.gate_up_proj": "packed_colwise",
+        "layers.*.mlp.experts.down_proj": "rowwise",
+        "layers.*.mlp.experts": "moe_tp_experts",
+        "layers.*.mlp.shared_experts.gate_proj": "colwise",
+        "layers.*.mlp.shared_experts.up_proj": "colwise",
+        "layers.*.mlp.shared_experts.down_proj": "rowwise",
+    }
+    base_model_pp_plan = {
+        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+        "norm": (["hidden_states"], ["hidden_states"]),
+    }
+
+    # Qwen2Moe-inherited defaults we want to override for Laguna's typical shape.
+    vocab_size: int = 100352
+    hidden_size: int = 2048
+    intermediate_size: int = 8192
+    num_hidden_layers: int = 40
+    num_attention_heads: int = 48
+    num_key_value_heads: int = 8
+    hidden_act: str = "silu"
+    max_position_embeddings: int = 131072
+    initializer_range: float = 0.02
+    rms_norm_eps: float = 1e-6
+    use_cache: bool = True
+    tie_word_embeddings: bool = False
+    rope_parameters: RopeParameters | dict | None = None
+    sliding_window: int | None = None
+    attention_dropout: float | int = 0.0
+    moe_intermediate_size: int = 512
+    shared_expert_intermediate_size: int = 512
+    num_experts_per_tok: int = 8
+    num_experts: int = 256
+    output_router_logits: bool = False
+    router_aux_loss_coef: float = 0.001
+    layer_types: list[str] | None = None
+    pad_token_id: int | None = None
+    bos_token_id: int | None = None
+    eos_token_id: int | list[int] | None = None
+
+    # Laguna-specific attention
+    head_dim: int = 128
+    attention_bias: bool = False
+    partial_rotary_factor: float | None = None
+    num_attention_heads_per_layer: list[int] | None = None
+    # Laguna-specific MoE
+    mlp_layer_types: list[str] | None = None
+    moe_routed_scaling_factor: float = 1.0
+    moe_apply_router_weight_on_input: bool = False
+    moe_router_logit_softcapping: float = 0.0
+
+    def __post_init__(self, **kwargs):
+        if self.layer_types is None:
+            self.layer_types = ["full_attention"] * self.num_hidden_layers
+        if self.mlp_layer_types is None:
+            self.mlp_layer_types = ["dense"] + ["sparse"] * (self.num_hidden_layers - 1)
+        if self.num_attention_heads_per_layer is None:
+            self.num_attention_heads_per_layer = [self.num_attention_heads] * self.num_hidden_layers
+
+        default_rope_params: dict[Literal["full_attention", "sliding_attention"], dict[str, Any]] = {
+            "full_attention": {"rope_type": "default", "rope_theta": 500000.0},
+            "sliding_attention": {"rope_type": "default", "rope_theta": 10000.0},
+        }
+        if self.rope_parameters is None:
+            self.rope_parameters = default_rope_params
+
+        self._normalize_rope_parameters()
+        # Skip ``Qwen2MoeConfig.__post_init__`` — it references ``mlp_only_layers`` /
+        # ``use_sliding_window`` / ``max_window_layers`` which Laguna drops above.
+        super().__post_init__(**kwargs)
+
+    def _normalize_rope_parameters(self):
+        """Coerce ``rope_parameters`` to the nested ``{layer_type: {...}}`` shape.
+
+        Accepts an already-nested dict as-is, or a flat dict that gets broadcast to every
+        layer type. A top-level ``partial_rotary_factor`` is folded into each sub-dict as
+        a default.
+        """
+        layer_types = set(self.layer_types)
+        rope_params = self.rope_parameters or {}
+        is_nested = isinstance(rope_params, dict) and any(k in layer_types for k in rope_params)
+        if is_nested:
+            nested = {lt: dict(rope_params.get(lt, {})) for lt in layer_types}
+        else:
+            nested = {lt: dict(rope_params) for lt in layer_types}
+
+        if self.partial_rotary_factor is not None:
+            for params in nested.values():
+                params.setdefault("partial_rotary_factor", self.partial_rotary_factor)
+
+        for params in nested.values():
+            params.setdefault("rope_type", "default")
+
+        self.rope_parameters = nested
+        # Null the top-level field now that its value lives in each sub-dict — otherwise
+        # ``standardize_rope_params`` would overwrite per-type values with the global one.
+        self.partial_rotary_factor = None
+
+    def convert_rope_params_to_dict(self, **kwargs):
+        # No need to handle BC for new models, because they have no old-format `rope_scaling`
+        return kwargs
+
+    def _validate_yarn_rope_parameters(self, rope_parameters: dict, ignore_keys=None):
+        """Override: parent reads ``self.rope_parameters["original_max_position_embeddings"]``
+        for its post-hoc factor sanity-check, which works for flat rope configs but raises
+        ``KeyError`` when ``self.rope_parameters`` is the Laguna/Gemma3-style per-layer-type
+        map (its keys are layer types like ``"full_attention"``). Fix locally by reading
+        from the per-call ``rope_parameters`` dict that ``validate_rope`` already passes in.
+        """
+        # Delegate to parent for the shared checks by temporarily swapping in a flat
+        # ``self.rope_parameters`` that has the key the parent expects. Cheapest way to
+        # share the parent's logic without reimplementing it here.
+        flat = getattr(self, "rope_parameters", None)
+        self.rope_parameters = rope_parameters
+        try:
+            super()._validate_yarn_rope_parameters(rope_parameters, ignore_keys=ignore_keys)
+        finally:
+            self.rope_parameters = flat
+
+    def validate_architecture(self):
+        """Part of ``@strict``-powered validation."""
+        if self.moe_apply_router_weight_on_input:
+            raise NotImplementedError(
+                "moe_apply_router_weight_on_input=True is not yet supported in the "
+                "transformers implementation of Laguna."
+            )
+        if (
+            self.num_attention_heads_per_layer is not None
+            and len(self.num_attention_heads_per_layer) != self.num_hidden_layers
+        ):
+            raise ValueError(
+                f"num_attention_heads_per_layer length ({len(self.num_attention_heads_per_layer)}) "
+                f"must equal num_hidden_layers ({self.num_hidden_layers})."
+            )
+        if len(self.layer_types) != self.num_hidden_layers:
+            raise ValueError(
+                f"layer_types length ({len(self.layer_types)}) "
+                f"must equal num_hidden_layers ({self.num_hidden_layers})."
+            )
+        if len(self.mlp_layer_types) != self.num_hidden_layers:
+            raise ValueError(
+                f"mlp_layer_types length ({len(self.mlp_layer_types)}) "
+                f"must equal num_hidden_layers ({self.num_hidden_layers})."
+            )
+
+
+__all__ = ["LagunaConfig"]

generation_config.json

@@ -0,0 +1,12 @@
+{
+  "bos_token_id": 2,
+  "do_sample": true,
+  "eos_token_id": [
+    2,
+    24
+  ],
+  "max_new_tokens": 2048,
+  "pad_token_id": 9,
+  "temperature": 0.7,
+  "top_p": 0.9
+}

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+size 4592303640

modeling_laguna.py

@@ -0,0 +1,755 @@
+# Copyright 2026 Poolside and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from collections.abc import Callable
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from transformers import initialization as init
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.generation import GenerationMixin
+from transformers.integrations import use_experts_implementation, use_kernel_forward_from_hub, use_kernelized_func
+from transformers.masking_utils import create_causal_mask, create_sliding_window_causal_mask
+from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
+from transformers.modeling_layers import GradientCheckpointingLayer
+from transformers.modeling_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast
+from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from transformers.processing_utils import Unpack
+from transformers.utils import auto_docstring, can_return_tuple
+from transformers.utils.generic import TransformersKwargs, maybe_autocast
+from transformers.utils.output_capturing import OutputRecorder, capture_outputs
+from .configuration_laguna import LagunaConfig
+
+
+@use_kernel_forward_from_hub("RMSNorm")
+class LagunaRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps: float = 1e-6) -> None:
+        """
+        LagunaRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+class LagunaRotaryEmbedding(nn.Module):
+    inv_freq: torch.Tensor  # fix linting for `register_buffer`
+
+    def __init__(self, config: LagunaConfig, device=None, layer_type=None):
+        super().__init__()
+        self.max_seq_len_cached = config.max_position_embeddings
+        self.original_max_seq_len = config.max_position_embeddings
+
+        self.config = config
+
+        self.layer_types = list(set(config.layer_types))
+        self.rope_type = {}
+        for layer_type in self.layer_types:
+            rope_params = self.config.rope_parameters[layer_type]
+            if rope_params is None:
+                continue
+
+            self.rope_type[layer_type] = rope_params["rope_type"]
+            rope_init_fn: Callable = self.compute_default_rope_parameters
+            if self.rope_type[layer_type] != "default":
+                rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type[layer_type]]
+            curr_inv_freq, curr_attention_scaling = rope_init_fn(self.config, device, layer_type=layer_type)
+            self.register_buffer(f"{layer_type}_inv_freq", curr_inv_freq, persistent=False)
+            self.register_buffer(f"{layer_type}_original_inv_freq", curr_inv_freq.clone(), persistent=False)
+            setattr(self, f"{layer_type}_attention_scaling", curr_attention_scaling)
+
+    @staticmethod
+    def compute_default_rope_parameters(
+        config: LagunaConfig | None = None,
+        device: Optional["torch.device"] = None,
+        seq_len: int | None = None,
+        layer_type: str | None = None,
+    ) -> tuple["torch.Tensor", float]:
+        """
+        Computes the inverse frequencies according to the original RoPE implementation
+        Args:
+            config ([`~transformers.PreTrainedConfig`]):
+                The model configuration.
+            device (`torch.device`):
+                The device to use for initialization of the inverse frequencies.
+            seq_len (`int`, *optional*):
+                The current sequence length. Unused for this type of RoPE.
+            layer_type (`str`, *optional*):
+                The current layer type if the model has different RoPE parameters per type.
+                Should not be used unless `config.layer_types is not None`
+        Returns:
+            Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+            post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
+        """
+        base = config.rope_parameters[layer_type]["rope_theta"]
+        # key difference to gemma3: partial rope
+        partial_rotary_factor = config.rope_parameters[layer_type].get("partial_rotary_factor", 1.0)
+        head_dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
+        dim = int(head_dim * partial_rotary_factor)
+
+        attention_factor = 1.0  # Unused in this type of RoPE
+
+        # Compute the inverse frequencies
+        inv_freq = 1.0 / (
+            base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
+        )
+        return inv_freq, attention_factor
+
+    @torch.no_grad()
+    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
+    def forward(self, x, position_ids, layer_type=None):
+        inv_freq = getattr(self, f"{layer_type}_inv_freq")
+        attention_scaling = getattr(self, f"{layer_type}_attention_scaling")
+
+        inv_freq_expanded = inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+        position_ids_expanded = position_ids[:, None, :].float()
+
+        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+        with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos() * attention_scaling
+            sin = emb.sin() * attention_scaling
+
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+class LagunaMLP(nn.Module):
+    def __init__(self, config, intermediate_size=None):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size if intermediate_size is None else intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+        return down_proj
+
+
+class LagunaTopKRouter(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.top_k = config.num_experts_per_tok
+        self.num_experts = config.num_experts
+        self.hidden_dim = config.hidden_size
+        self.weight = nn.Parameter(torch.zeros(self.num_experts, self.hidden_dim))
+        self.e_score_correction_bias = nn.Parameter(torch.zeros(config.num_experts), requires_grad=False)
+        self.router_logit_softcapping = config.moe_router_logit_softcapping
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        hidden_states = hidden_states.reshape(-1, self.hidden_dim)
+        router_logits = F.linear(hidden_states, self.weight).float()
+        # Optional logits softcapping
+        if self.router_logit_softcapping > 0.0:
+            router_logits = torch.tanh(router_logits / self.router_logit_softcapping) * self.router_logit_softcapping
+        # Sigmoid instead of softmax normalization
+        routing_scores = torch.sigmoid(router_logits)
+
+        scores_for_selection = routing_scores + self.e_score_correction_bias.to(routing_scores.dtype)
+        _, selected_experts = torch.topk(scores_for_selection, self.top_k, dim=-1)
+        routing_weights = routing_scores.gather(-1, selected_experts)
+        routing_weights = routing_weights / routing_weights.sum(dim=-1, keepdim=True)
+        routing_weights = routing_weights.to(hidden_states.dtype)
+
+        return router_logits, routing_weights, selected_experts
+
+
+@use_experts_implementation
+class LagunaExperts(nn.Module):
+    """Collection of expert weights stored as 3D tensors."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.num_experts = config.num_experts
+        self.hidden_dim = config.hidden_size
+        self.intermediate_dim = config.moe_intermediate_size
+        self.gate_up_proj = nn.Parameter(torch.empty(self.num_experts, 2 * self.intermediate_dim, self.hidden_dim))
+        self.down_proj = nn.Parameter(torch.empty(self.num_experts, self.hidden_dim, self.intermediate_dim))
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        top_k_index: torch.Tensor,
+        top_k_weights: torch.Tensor,
+    ) -> torch.Tensor:
+        final_hidden_states = torch.zeros_like(hidden_states)
+        with torch.no_grad():
+            expert_mask = torch.nn.functional.one_hot(top_k_index, num_classes=self.num_experts)
+            expert_mask = expert_mask.permute(2, 1, 0)
+            expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero()
+
+        for expert_idx in expert_hit:
+            expert_idx = expert_idx[0]
+            if expert_idx == self.num_experts:
+                continue
+            top_k_pos, token_idx = torch.where(expert_mask[expert_idx])
+            current_state = hidden_states[token_idx]
+            gate, up = nn.functional.linear(current_state, self.gate_up_proj[expert_idx]).chunk(2, dim=-1)
+            current_hidden_states = self.act_fn(gate) * up
+            current_hidden_states = nn.functional.linear(current_hidden_states, self.down_proj[expert_idx])
+            current_hidden_states = current_hidden_states * top_k_weights[token_idx, top_k_pos, None]
+            final_hidden_states.index_add_(0, token_idx, current_hidden_states.to(final_hidden_states.dtype))
+
+        return final_hidden_states
+
+
+class LagunaSparseMoeBlock(nn.Module):
+    def __init__(self, config: LagunaConfig):
+        super().__init__()
+        self.experts = LagunaExperts(config)
+        self.gate = LagunaTopKRouter(config)
+        self.shared_experts = LagunaMLP(config, intermediate_size=config.shared_expert_intermediate_size)
+        self.routed_scaling_factor = config.moe_routed_scaling_factor
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        batch_size, sequence_length, hidden_dim = hidden_states.shape
+        hidden_states = hidden_states.view(-1, hidden_dim)
+        shared_output = self.shared_experts(hidden_states)
+
+        _, routing_weights, selected_experts = self.gate(hidden_states)
+        hidden_states = self.experts(hidden_states, selected_experts, routing_weights)
+        # Additional scaling
+        hidden_states = hidden_states * self.routed_scaling_factor
+        hidden_states = hidden_states + shared_output
+
+        hidden_states = hidden_states.reshape(batch_size, sequence_length, hidden_dim)
+        return hidden_states
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+# Adapted from transformers.models.glm.modular_glm.apply_rotary_pos_emb
+def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Removes the interleaving of cos and sin from GLM
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+
+    # Keep half or full tensor for later concatenation
+    rotary_dim = cos.shape[-1]
+    q_rot, q_pass = q[..., :rotary_dim], q[..., rotary_dim:]
+    k_rot, k_pass = k[..., :rotary_dim], k[..., rotary_dim:]
+
+    # Apply rotary embeddings on the first half or full tensor
+    q_embed = (q_rot * cos) + (rotate_half(q_rot) * sin)
+    k_embed = (k_rot * cos) + (rotate_half(k_rot) * sin)
+
+    # Concatenate back to full shape
+    q_embed = torch.cat([q_embed, q_pass], dim=-1)
+    k_embed = torch.cat([k_embed, k_pass], dim=-1)
+    return q_embed, k_embed
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+def eager_attention_forward(
+    module: nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: torch.Tensor | None,
+    scaling: float,
+    dropout: float = 0.0,
+    **kwargs: Unpack[TransformersKwargs],
+):
+    key_states = repeat_kv(key, module.num_key_value_groups)
+    value_states = repeat_kv(value, module.num_key_value_groups)
+
+    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+    if attention_mask is not None:
+        attn_weights = attn_weights + attention_mask
+
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+    attn_output = torch.matmul(attn_weights, value_states)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    return attn_output, attn_weights
+
+
+@use_kernelized_func(apply_rotary_pos_emb)
+class LagunaAttention(nn.Module):
+    """Afmoe-style SWA/GQA attention with Laguna-specific gating and per-layer head count."""
+
+    def __init__(self, config: LagunaConfig, layer_idx: int, num_heads: int):
+        super().__init__()
+        # Number of heads is controlled via `config.num_attention_heads_per_layer` which is passed from the parent for the specific layer
+        self.num_heads = num_heads
+        self.config = config
+        self.layer_idx = layer_idx
+        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+        self.num_key_value_groups = self.num_heads // config.num_key_value_heads
+        self.scaling = self.head_dim**-0.5
+        self.attention_dropout = config.attention_dropout
+        self.is_causal = True
+
+        # Per-layer head count: rebuild q_proj and o_proj using self.num_heads (parent uses config.num_attention_heads).
+        self.q_proj = nn.Linear(config.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
+        self.k_proj = nn.Linear(
+            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
+        )
+        self.v_proj = nn.Linear(
+            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
+        )
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, config.hidden_size, bias=config.attention_bias)
+        # Parent LlamaAttention already sets: layer_idx, num_heads, num_key_value_heads, num_key_value_groups, head_dim
+        # We only add Laguna-specific attributes
+        self.is_local_attention = config.layer_types[layer_idx] == "sliding_attention"
+        self.sliding_window = config.sliding_window if self.is_local_attention else None
+
+        self.q_norm = LagunaRMSNorm(self.head_dim, eps=config.rms_norm_eps)
+        self.k_norm = LagunaRMSNorm(self.head_dim, eps=config.rms_norm_eps)
+        self.g_proj = nn.Linear(config.hidden_size, self.num_heads, bias=False)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: torch.Tensor | None,
+        past_key_values: Cache | None = None,
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        query_states = self.q_proj(hidden_states).view(hidden_shape)
+        key_states = self.k_proj(hidden_states).view(hidden_shape)
+        value_states = self.v_proj(hidden_states).view(hidden_shape)
+
+        query_states = self.q_norm(query_states).transpose(1, 2)
+        key_states = self.k_norm(key_states).transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_values is not None:
+            key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx)
+
+        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
+            self.config._attn_implementation, eager_attention_forward
+        )
+        attn_output, attn_weights = attention_interface(
+            self,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            dropout=0.0 if not self.training else self.attention_dropout,
+            scaling=self.scaling,
+            sliding_window=self.sliding_window,
+            **kwargs,
+        )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+
+        gate = F.softplus(self.g_proj(hidden_states).float()).to(attn_output.dtype)
+        attn_output = (attn_output.view(*input_shape, -1, self.head_dim) * gate.unsqueeze(-1)).view(*input_shape, -1)
+
+        attn_output = self.o_proj(attn_output)
+        return attn_output, attn_weights
+
+
+class LagunaDecoderLayer(GradientCheckpointingLayer):
+    def __init__(self, config: LagunaConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = LagunaAttention(config, layer_idx, config.num_attention_heads_per_layer[layer_idx])
+        if config.mlp_layer_types[layer_idx] == "sparse":
+            self.mlp = LagunaSparseMoeBlock(config)
+        else:
+            self.mlp = LagunaMLP(config, intermediate_size=config.intermediate_size)
+        self.input_layernorm = LagunaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = LagunaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        use_cache: bool | None = False,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        # Self Attention
+        hidden_states, _ = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            position_embeddings=position_embeddings,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+        return hidden_states
+
+
+@auto_docstring
+class LagunaPreTrainedModel(PreTrainedModel):
+    config: LagunaConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["LagunaDecoderLayer"]
+    _skip_keys_device_placement = ["past_key_values"]
+    _supports_flash_attn = True
+    _supports_sdpa = True
+    _supports_flex_attn = True
+
+    _can_compile_fullgraph = True
+    _supports_attention_backend = True
+    _can_record_outputs = {
+        "router_logits": OutputRecorder(LagunaTopKRouter, index=0),
+        "hidden_states": LagunaDecoderLayer,
+        "attentions": LagunaAttention,
+    }
+
+    @torch.no_grad()
+    def _init_weights(self, module):
+        super()._init_weights(module)
+        std = self.config.initializer_range
+        if isinstance(module, LagunaExperts):
+            init.normal_(module.gate_up_proj, mean=0.0, std=std)
+            init.normal_(module.down_proj, mean=0.0, std=std)
+        elif isinstance(module, LagunaTopKRouter):
+            init.normal_(module.weight, mean=0.0, std=std)
+        if isinstance(module, LagunaTopKRouter):
+            torch.nn.init.zeros_(module.e_score_correction_bias)
+        elif isinstance(module, LagunaRotaryEmbedding):
+            for layer_type in module.layer_types:
+                rope_init_fn = module.compute_default_rope_parameters
+                if module.rope_type[layer_type] != "default":
+                    rope_init_fn = ROPE_INIT_FUNCTIONS[module.rope_type[layer_type]]
+                curr_inv_freq, _ = rope_init_fn(module.config, layer_type=layer_type)
+                init.copy_(getattr(module, f"{layer_type}_inv_freq"), curr_inv_freq)
+                init.copy_(getattr(module, f"{layer_type}_original_inv_freq"), curr_inv_freq)
+
+
+@auto_docstring
+class LagunaModel(LagunaPreTrainedModel):
+    def __init__(self, config: LagunaConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList(
+            [LagunaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.norm = LagunaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = LagunaRotaryEmbedding(config=config)
+        self.gradient_checkpointing = False
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @capture_outputs
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        use_cache: bool | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> MoeModelOutputWithPast:
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        if use_cache and past_key_values is None:
+            past_key_values = DynamicCache(config=self.config)
+
+        if position_ids is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens
+            position_ids = position_ids.unsqueeze(0)
+
+        if not isinstance(causal_mask_mapping := attention_mask, dict):
+            mask_kwargs = {
+                "config": self.config,
+                "inputs_embeds": inputs_embeds,
+                "attention_mask": attention_mask,
+                "past_key_values": past_key_values,
+                "position_ids": position_ids,
+            }
+            mask_creation_functions = {
+                "full_attention": lambda: create_causal_mask(**mask_kwargs),
+                "sliding_attention": lambda: create_sliding_window_causal_mask(**mask_kwargs),
+            }
+            causal_mask_mapping = {}
+            for layer_type in set(self.config.layer_types):
+                causal_mask_mapping[layer_type] = mask_creation_functions[layer_type]()
+
+        hidden_states = inputs_embeds
+        position_embeddings = {}
+        for layer_type in set(self.config.layer_types):
+            position_embeddings[layer_type] = self.rotary_emb(hidden_states, position_ids, layer_type)
+
+        for i, decoder_layer in enumerate(self.layers[: self.config.num_hidden_layers]):
+            hidden_states = decoder_layer(
+                hidden_states,
+                attention_mask=causal_mask_mapping[self.config.layer_types[i]],
+                position_embeddings=position_embeddings[self.config.layer_types[i]],
+                position_ids=position_ids,
+                past_key_values=past_key_values,
+                **kwargs,
+            )
+
+        hidden_states = self.norm(hidden_states)
+
+        return MoeModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values if use_cache else None,
+        )
+
+
+def load_balancing_loss_func(
+    gate_logits: torch.Tensor | tuple[torch.Tensor] | None,
+    num_experts: int | None = None,
+    top_k=2,
+    attention_mask: torch.Tensor | None = None,
+) -> torch.Tensor | int:
+    r"""
+    Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
+
+    See Switch Transformer (https://huggingface.co/papers/2101.03961) for more details. This function implements the loss
+    function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
+    experts is too unbalanced.
+
+    Args:
+        gate_logits:
+            Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
+            shape [batch_size X sequence_length, num_experts].
+        num_experts:
+            Number of experts
+        top_k:
+            The number of experts to route per-token, can be also interpreted as the `top-k` routing
+            parameter.
+        attention_mask (`torch.Tensor`, *optional*):
+            The attention_mask used in forward function
+            shape [batch_size X sequence_length] if not None.
+
+    Returns:
+        The auxiliary loss.
+    """
+    if gate_logits is None or not isinstance(gate_logits, tuple):
+        return 0
+
+    if isinstance(gate_logits, tuple):
+        compute_device = gate_logits[0].device
+        concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
+
+    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+
+    _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
+
+    expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)
+
+    if attention_mask is None:
+        # Compute the percentage of tokens routed to each experts
+        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+
+        # Compute the average probability of routing to these experts
+        router_prob_per_expert = torch.mean(routing_weights, dim=0)
+    else:
+        batch_size, sequence_length = attention_mask.shape
+        num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
+
+        # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
+        expert_attention_mask = (
+            attention_mask[None, :, :, None, None]
+            .expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts))
+            .reshape(-1, top_k, num_experts)
+            .to(compute_device)
+        )
+
+        # Compute the percentage of tokens routed to each experts
+        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
+            expert_attention_mask, dim=0
+        )
+
+        # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
+        router_per_expert_attention_mask = (
+            attention_mask[None, :, :, None]
+            .expand((num_hidden_layers, batch_size, sequence_length, num_experts))
+            .reshape(-1, num_experts)
+            .to(compute_device)
+        )
+
+        # Compute the average probability of routing to these experts
+        router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
+            router_per_expert_attention_mask, dim=0
+        )
+
+    overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0))
+    return overall_loss * num_experts
+
+
+@auto_docstring
+class LagunaForCausalLM(LagunaPreTrainedModel, GenerationMixin):
+    _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"}
+    _tp_plan = {"lm_head": "colwise_gather_output"}
+    _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = LagunaModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.router_aux_loss_coef = config.router_aux_loss_coef
+        self.num_experts = config.num_experts
+        self.num_experts_per_tok = config.num_experts_per_tok
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        output_router_logits: bool | None = None,
+        logits_to_keep: int | torch.Tensor = 0,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> MoeCausalLMOutputWithPast:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+        """
+
+        output_router_logits = (
+            output_router_logits if output_router_logits is not None else self.config.output_router_logits
+        )
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs: MoeModelOutputWithPast = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_router_logits=output_router_logits,
+            **kwargs,
+        )
+
+        hidden_states = outputs.last_hidden_state
+        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits, labels, self.vocab_size, **kwargs)
+
+        aux_loss = None
+        if output_router_logits:
+            aux_loss = load_balancing_loss_func(
+                outputs.router_logits,
+                self.num_experts,
+                self.num_experts_per_tok,
+                attention_mask,
+            )
+            if labels is not None:
+                loss += self.router_aux_loss_coef * aux_loss.to(loss.device)  # make sure to reside in the same device
+
+        return MoeCausalLMOutputWithPast(
+            loss=loss,
+            aux_loss=aux_loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            router_logits=outputs.router_logits,
+        )
+
+
+__all__ = ["LagunaForCausalLM", "LagunaModel", "LagunaPreTrainedModel"]

special_tokens_map.json

@@ -0,0 +1,9 @@
+{
+  "bos_token": "〈|EOS|〉",
+  "cls_token": "〈|CLS|〉",
+  "eos_token": "〈|EOS|〉",
+  "mask_token": "〈|MASK|〉",
+  "pad_token": "〈|PAD|〉",
+  "sep_token": "〈|SEP|〉",
+  "unk_token": "〈|UNK|〉"
+}

tokenizer_config.json

@@ -0,0 +1,576 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "〈|UNK|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "〈|CODE_START|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "〈|EOS|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "〈|CODE_END|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "〈|META_START|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "5": {
+      "content": "〈|META_END|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "6": {
+      "content": "〈|FIM_MIDDLE|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "7": {
+      "content": "〈|FIM_SUFFIX|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "8": {
+      "content": "〈|SEP|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "9": {
+      "content": "〈|PAD|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "10": {
+      "content": "〈|CLS|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "11": {
+      "content": "〈|FIM_START|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "12": {
+      "content": "〈|MASK|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "13": {
+      "content": "|◊|",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "14": {
+      "content": "〈|",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "15": {
+      "content": "|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "16": {
+      "content": "〈|/",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "17": {
+      "content": "/|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "20": {
+      "content": "〈|SPECIAL_1|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "21": {
+      "content": "〈|SPECIAL_2|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "22": {
+      "content": "〈|SPECIAL_3|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "27": {
+      "content": "〈|SPECIAL_8|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "28": {
+      "content": "〈|SPECIAL_9|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "29": {
+      "content": "〈|SPECIAL_10|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "30": {
+      "content": "〈|SPECIAL_11|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "31": {
+      "content": "〈|SPECIAL_12|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32": {
+      "content": "〈|SPECIAL_13|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "33": {
+      "content": "〈|SPECIAL_14|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "34": {
+      "content": "〈|SPECIAL_15|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "35": {
+      "content": "〈|SPECIAL_16|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "36": {
+      "content": "〈|SPECIAL_17|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "37": {
+      "content": "〈|SPECIAL_18|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "38": {
+      "content": "〈|SPECIAL_19|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "39": {
+      "content": "〈|SPECIAL_20|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "40": {
+      "content": "〈|SPECIAL_21|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "41": {
+      "content": "〈|SPECIAL_22|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "42": {
+      "content": "〈|SPECIAL_23|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "43": {
+      "content": "〈|SPECIAL_24|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "44": {
+      "content": "〈|SPECIAL_25|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "45": {
+      "content": "〈|SPECIAL_26|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
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+    },
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+    },
+    "60": {
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+    },
+    "61": {
+      "content": "〈|SPECIAL_42|〉",
+      "lstrip": false,
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+    },
+    "62": {
+      "content": "〈|SPECIAL_43|〉",
+      "lstrip": false,
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+    "63": {
+      "content": "〈|SPECIAL_44|〉",
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+    "64": {
+      "content": "〈|SPECIAL_45|〉",
+      "lstrip": false,
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+    },
+    "65": {
+      "content": "〈|SPECIAL_46|〉",
+      "lstrip": false,
+      "normalized": false,
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+    },
+    "66": {
+      "content": "〈|SPECIAL_47|〉",
+      "lstrip": false,
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+    },
+    "67": {
+      "content": "〈|SPECIAL_48|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
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+    },
+    "68": {
+      "content": "〈|SPECIAL_49|〉",
+      "lstrip": false,
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+    },
+    "69": {
+      "content": "〈|SPECIAL_50|〉",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "18": {
+      "content": "<think>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "19": {
+      "content": "</think>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "23": {
+      "content": "<assistant>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "24": {
+      "content": "</assistant>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "25": {
+      "content": "<tool_call>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "26": {
+      "content": "</tool_call>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    }
+  },
+  "bos_token": "〈|EOS|〉",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "〈|CLS|〉",
+  "eos_token": "〈|EOS|〉",
+  "extra_special_tokens": {},
+  "mask_token": "〈|MASK|〉",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "〈|PAD|〉",
+  "sep_token": "〈|SEP|〉",
+  "tokenizer_class": "PreTrainedTokenizerFast",
+  "unk_token": "〈|UNK|〉",
+  "chat_template": "{% include 'chat_template.jinja' %}"
+}