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Model: jpacifico/Chocolatine-2-4B-Instruct-DPO-v2.1 Source: Original Platform
2026-04-10 18:45:01 +08:00
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--- a/README.md
+++ b/README.md
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+---
+language:
+- fr
+- en
+library_name: transformers
+tags:
+- dpo
+- post-training
+- french
+- alignment
+- model-merging
+- qwen3
+- chocolatine
+- comparia
+license: apache-2.0
+base_model: Qwen/Qwen3-4B-Instruct-2507
+datasets:
+- jpacifico/comparia-dpo-pairs-bt-6k
+- jpacifico/french-orca-dpo-pairs-revised
+---
+
+# Chocolatine-2-4B-Instruct-DPO-v2.1
+
+**Chocolatine-2-4B-Instruct-DPO-v2.1** is a post-trained version of [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507), designed to improve instruction-following, reasoning, and overall performance in French, while preserving strong multilingual capabilities.  
+In my evaluation setup, it delivers consistent gains across the tested French benchmarks, pointing to a broad improvement in French capabilities.  
+Although the post-training pipeline focuses on French preference data, no degradation is observed on English tasks, and slight improvements are sometimes seen, suggesting positive cross-lingual transfer.  
+Optimized variants (MLX, GGUF) are also available, making the model particularly suitable for local inference.  
+
+
+## Model Overview
+
+- **Base model:** Qwen/Qwen3-4B-Instruct-2507
+- **Parameters:** 4.0B
+- **Context Length:** 262,144 natively
+- **Post training methods:** DPO + Model Merging
+
+Note: This model supports only non-thinking mode and does not generate `<think></think>` blocks in its outputs.  
+This design is consistent with the goals of the post-training setup, which favors a compact dense instruct model focused on direct generation efficiency and practical downstream use.    
+For use cases requiring explicit reasoning traces or structured thinking outputs, Qwen/Qwen3.5-4B (thinking mode) is recommended.
+
+**Model Variants**
+
+- Chocolatine-2-4B-Instruct-DPO-v2.1 (this repo): Contains the retrainable weights in BF16 format
+- Quantized GGUF versions : [Q4_K_M](https://huggingface.co/jpacifico/Chocolatine-2-4B-Instruct-DPO-v2.1-Q4_K_M-GGUF) / [Q8_0](https://huggingface.co/jpacifico/Chocolatine-2-4B-Instruct-DPO-v2.1-Q8_0-GGUF) and more from mradermacher [here](https://huggingface.co/mradermacher/Chocolatine-2-4B-Instruct-DPO-v2.1-GGUF)
+- MLX (optimized for Apple silicon): [4Bit](https://huggingface.co/jpacifico/Chocolatine-2-4B-Instruct-DPO-v2.1-mlx-4Bit) / [6Bit](https://huggingface.co/jpacifico/Chocolatine-2-4B-Instruct-DPO-v2.1-mlx-8Bit)
+
+**Ollama** : In addition to the Hugging Face release, quantized 4-bit and 8-bit variants are also available [here](https://ollama.com/jpacifico/chocolatine-2.1) on Ollama for convenient local inference.
+
+## Benchmarks
+
+The results indicate a consistent improvement across the tested French benchmarks, covering several capability types. This suggests a broad gain in French performance, while English results remain overall stable.
+
+| Benchmark fr | Qwen3-4B-Instruct-2507 (base) | Chocolatine-2-4B-Instruct-DPO-v2.1 |
+|---|---:|---:|
+| gpqa-fr:diamond | 28.93 | **32.49** |
+| french_bench_arc_challenge | 47.13 | **49.79** |
+| french_bench_grammar | 70.59 | **72.27** |
+| french_bench_boolqa | 88.76 | **89.89** |
+| french_bench_hellaswag | 56.99 | **58.03** |
+| global_mmlu_fr | 63.75 | **64.75** |
+| xwinograd_fr | 66.27 | **67.47** |
+| fr_mt_bench | 6.22 | **6.44** |
+
+*FR-MT-Bench* evaluation is performed on [MT-Bench-French](https://huggingface.co/datasets/bofenghuang/mt-bench-french), using [multilingual-mt-bench](https://github.com/jpacifico/multilingual_mt_bench) with OpenAI/GPT-5 as the LLM judge.  
+*global_mmlu_fr*, *xwinograd_fr* and *french_bench* results were obtained using [EleutherAI LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness) in a **0-shot** evaluation setting.  
+*gpqa-fr:diamond* using LightEval/vLLM via [kurakurai/Luth](https://github.com/kurakurai/Luth.git) process eval.
+
+| Benchmark eng | Qwen3-4B-Instruct-2507 (base) | Chocolatine-2-4B-Instruct-DPO-v2.1 |
+|---|---:|---:|
+| arc_challenge | **58.79** | 58.45 |
+| hellaswag | 69.08 | **70.16** |
+| boolq | 84.80 | **85.32** |
+| gpqa_diamond_zeroshot | **38.89** | 38.38 |
+
+English benchmark results were obtained using [EleutherAI LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness) in a **0-shot** evaluation setting.
+
+## Training & Alignment Pipeline  
+
+Chocolatine-2-4B-Instruct-DPO-v2.1 is derived from [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507) using a multi-step post-training pipeline:
+
+**Stage 1 – DPO (Compar:IA adaptation)**
+
+Direct Preference Optimization (DPO) on a DPO-adapted version of **[Compar:IA](https://comparia.beta.gouv.fr/datasets)** data, derived from the preference dataset [comparia-votes](https://huggingface.co/datasets/ministere-culture/comparia-votes), part of a public initiative led by the Ministry of Culture (French gov). Previous iterations of the Chocolatine model series also were selected as part of this initiative.  
+I constructed an original DPO dataset from these votes by transforming them into preference pairs (chosen / rejected), with additional filtering and formatting steps to make them suitable for DPO fine-tuning.  
+Two dataset variants were created ([6k](https://huggingface.co/datasets/jpacifico/comparia-dpo-pairs-bt-6k) and [13k](https://huggingface.co/datasets/jpacifico/comparia-dpo-pairs-bt-13k) preference pairs).  
+The **6k variant** was used for the DPO training reported in this release.
+
+**Stage 2 – DPO (French-ORCA pairs)**
+
+A second DPO stage using a french-version of ORCA preference pairs, based on the dataset **[jpacifico/french-orca-dpo-pairs-revised](https://huggingface.co/datasets/jpacifico/french-orca-dpo-pairs-revised)**, commonly used in the Chocolatine training pipeline.  
+This stage further improves : general instruction alignment, robustness across tasks, cross-lingual capabilities. 
+
+**Stage 3 – Model Merging (MergeKit + TIES)**
+
+The resulting checkpoints were merged using **MergeKit** with the TIES method.
+
+TIES merging: selects task-relevant parameter updates, reduces destructive interference between models and preserves base model stability.
+
+MergeKit configuration:
+
+```yaml
+# ties2 recipe
+models:
+  - model: jpacifico/Qwen3-4B-Instruct-DPO-test2
+    parameters:
+      density: 0.5
+      weight: 0.5
+  - model: jpacifico/Qwen3-4B-Instruct-DPO-test-b3
+    parameters:
+      density: 0.5
+      weight: 0.5
+
+merge_method: ties
+base_model: Qwen/Qwen3-4B-Instruct-2507
+
+parameters:
+  normalize: false
+  int8_mask: true
+
+dtype: bfloat16
+```
+
+## Usage
+
+The following contains a code snippet illustrating how to use the model generate content based on given inputs.  
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "jpacifico/Chocolatine-2-4B-Instruct-DPO-v2.1"
+
+# load the tokenizer and the model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+# prepare the model input
+prompt = "Give me a short introduction to large language model."
+messages = [
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+# conduct text completion
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=16384
+)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 
+
+content = tokenizer.decode(output_ids, skip_special_tokens=True)
+
+print("content:", content)
+```
+
+## Limitations  
+
+The Chocolatine-2 model series is a quick demonstration that a base model can be easily fine-tuned to achieve compelling performance.
+It does not have any moderation mechanism.  
+
+Developed by: Jonathan Pacifico, 2026   
+Model type: LLM  
+Language(s) (NLP): French, English  
+License: Apache-2.0  
+
+Made with ❤️ in France
--- a/config.json
+++ b/config.json
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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
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+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 9728,
+  "layer_types": [
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+    "full_attention",
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+  "max_position_embeddings": 262144,
+  "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": 5000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.53.1",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}
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+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.56.1"
+}
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+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        },
+        "151666": {
+            "content": "</tool_response>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        },
+        "151667": {
+            "content": "<think>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        },
+        "151668": {
+            "content": "</think>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        }
+    },
+    "additional_special_tokens": [
+        "<|im_start|>",
+        "<|im_end|>",
+        "<|object_ref_start|>",
+        "<|object_ref_end|>",
+        "<|box_start|>",
+        "<|box_end|>",
+        "<|quad_start|>",
+        "<|quad_end|>",
+        "<|vision_start|>",
+        "<|vision_end|>",
+        "<|vision_pad|>",
+        "<|image_pad|>",
+        "<|video_pad|>"
+    ],
+    "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{%- 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        {{- '<|im_start|>' + message.role + '\\n' + content }}\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' }}\n{%- endif %}",
+    "clean_up_tokenization_spaces": false,
+    "eos_token": "<|im_end|>",
+    "errors": "replace",
+    "model_max_length": 1010000,
+    "pad_token": "<|endoftext|>",
+    "split_special_tokens": false,
+    "tokenizer_class": "Qwen2Tokenizer",
+    "unk_token": null,
+    "add_bos_token": false
+}
				`@@ -0,0 +1 @@`
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