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Model: lightonai/Qwen3-8B-FR
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README.md

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+---
+library_name: transformers
+base_model: Qwen/Qwen3-8B-Base
+tags:
+- multilingual
+- reasoning
+- LLM
+- qwen3
+license: apache-2.0
+datasets:
+- lightonai/Dolci-Think-SFT-32B-Multilingual
+language:
+- fr
+pipeline_tag: text-generation
+---
+
+# Qwen3-8B-FR
+
+`Qwen3-8B-FR` is a **native reasoning model** fine-tuned from [`Qwen/Qwen3-8B-Base`](https://huggingface.co/Qwen/Qwen3-8B-Base) to reason in French. This model produces its **entire reasoning trace in French** before delivering the final answer in French.
+
+It is released alongside the paper [**Rethinking the Multilingual Reasoning Gap with Layer Swap**](https://arxiv.org/abs/2605.26735).
+
+## Model details
+
+- **Base model:** `Qwen/Qwen3-8B-Base`
+- **Language:** French (CoT and answer)
+- **Training:** Full SFT, ~10B tokens, 2 epochs
+- **Context length:** 32,768 tokens
+- **Dataset:** [`lightonai/Dolci-Think-SFT-32B-Multilingual`](https://huggingface.co/datasets/lightonai/Dolci-Think-SFT-32B-Multilingual) (French split).
+
+> [!NOTE]
+> The model was trained on data derived from `allenai/Dolci-Think-SFT-32B`, released under the ODC-BY-1.0 license.
+
+## Related models
+
+This model is part of a French specialist trio designed to study the native reasoning gap:
+
+| Model | CoT language | Description |
+|---|---|---|
+| [`lightonai/Qwen3-8B-FR`](https://huggingface.co/lightonai/Qwen3-8B-FR) | French | Native reasoning specialist |
+| [`lightonai/Qwen3-8B-FR-Swap`](https://huggingface.co/lightonai/Qwen3-8B-FR-Swap) | French | Layer Swap: middle layers (L13–L22) of `Qwen3-8B-EN` transplanted into `Qwen3-8B-FR` |
+| [`lightonai/Qwen3-8B-FR-Pivot-EN`](https://huggingface.co/lightonai/Qwen3-8B-FR-Pivot-EN) | English | Same French Q&A pairs, but CoT in English |
+| [`lightonai/Qwen3-8B-EN`](https://huggingface.co/lightonai/Qwen3-8B-EN) | English | English specialist |
+
+## Evaluation
+
+All scores are mean accuracy (%) on the **French** version of each benchmark, with sample standard deviation across runs. AIME 24/25 is averaged over 30 runs; the others over 10 runs, using the recommended generation parameters.
+
+| Model | MGSM-Rev2 | Global-MMLU-Lite | GPQA-Diamond | AIME 24/25 | HumanEvalPlus | Average |
+|---|:---:|:---:|:---:|:---:|:---:|:---:|
+| `Qwen3-8B-FR` | 92.80 | 76.45 | 53.59 | 55.67 | 83.31 | 72.36 |
+| `Qwen3-8B-FR-Swap` | <u>97.40</u> | 76.57 | 54.55 | 59.11 | <u>86.06</u> | 74.74 |
+| `Qwen3-8B-FR-Pivot-EN` | 94.52 | <u>78.37</u> | <u>54.65</u> | <u>62.78</u> | 84.88 | <u>75.04</u> |
+| `Qwen3-8B-EN` | 95.72 | 77.50 | 52.53 | 61.39 | 84.19 | 74.27 |
+
+**Benchmarks used:**
+
+- [`lightonai/gpqa_diamond_multilingual`](https://huggingface.co/datasets/lightonai/gpqa_diamond_multilingual)
+- [`lightonai/aime24_multilingual`](https://huggingface.co/datasets/lightonai/aime24_multilingual)
+- [`lightonai/aime25_multilingual`](https://huggingface.co/datasets/lightonai/aime25_multilingual)
+- [`lightonai/HumanEvalPlus_multilingual`](https://huggingface.co/datasets/lightonai/HumanEvalPlus_multilingual)
+- [`lightonai/mgsm-rev2`](https://huggingface.co/datasets/lightonai/mgsm-rev2)
+- [`CohereLabs/Global-MMLU-Lite`](https://huggingface.co/datasets/CohereLabs/Global-MMLU-Lite)
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "lightonai/Qwen3-8B-FR"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
+
+messages = [{"role": "user", "content": "Résous : 24 × 17 = ?"}]
+inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)
+
+outputs = model.generate(inputs, max_new_tokens=32768, temperature=1.0, top_p=0.95, top_k=20)
+print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))
+```
+
+Recommended sampling: `temperature=1.0`, `top_p=0.95`, `top_k=20`, `min_p=0`.
+
+## Citation
+
+If you find our work helpful, feel free to give us a cite.
+
+```bibtex
+@misc{lasbordes2026rethinking,
+  title        = {Rethinking the Multilingual Reasoning Gap with Layer Swap},
+  author       = {Lasbordes, Maxence and Chatelain, Amélie and Seddah, Djamé},
+  year         = {2026},
+  eprint       = {2605.26735},
+  archivePrefix= {arXiv},
+  primaryClass = {cs.CL}
+}
+```

config.json

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+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 12288,
+  "max_position_embeddings": 40960,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+    "do_sample": true,
+    "eos_token_id": [
+        151645,
+        151643
+    ],
+    "pad_token_id": 151643,
+    "temperature": 1,
+    "top_k": 20,
+    "top_p": 0.95,
+    "transformers_version": "4.51.0"
+}

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+  ],
+  "bos_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0].role == 'system' %}\n        {{- messages[0].content + '\\n\\n' }}\n    {%- endif %}\n    {{- \"# 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>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\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\" }}\n{%- else %}\n    {%- if messages[0].role == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n    {%- set index = (messages|length - 1) - loop.index0 %}\n    {%- 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>')) %}\n        {%- set ns.multi_step_tool = false %}\n        {%- set ns.last_query_index = index %}\n    {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n    {%- if message.content is string %}\n        {%- set content = message.content %}\n    {%- else %}\n        {%- set content = '' %}\n    {%- endif %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n        {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {%- set reasoning_content = '' %}\n        {%- if message.reasoning_content is string %}\n            {%- set reasoning_content = message.reasoning_content %}\n        {%- else %}\n            {%- if '</think>' in content %}\n                {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n                {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n            {%- endif %}\n        {%- endif %}\n        {%- if loop.index0 > ns.last_query_index %}\n            {%- if loop.last or (not loop.last and reasoning_content) %}\n                {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n            {%- else %}\n                {{- '<|im_start|>' + message.role + '\\n' + content }}\n            {%- endif %}\n        {%- else %}\n            {{- '<|im_start|>' + message.role + '\\n' + content }}\n        {%- endif %}\n        {%- if message.tool_calls %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if (loop.first and content) or (not loop.first) %}\n                    {{- '\\n' }}\n                {%- endif %}\n                {%- if tool_call.function %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {{- '<tool_call>\\n{\"name\": \"' }}\n                {{- tool_call.name }}\n                {{- '\", \"arguments\": ' }}\n                {%- if tool_call.arguments is string %}\n                    {{- tool_call.arguments }}\n                {%- else %}\n                    {{- tool_call.arguments | tojson }}\n                {%- endif %}\n                {{- '}\\n</tool_call>' }}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n<think>\\n' }}\n{%- endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}