Instructions to use jpacifico/Chocolatine-2-4B-Instruct-DPO-v2.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jpacifico/Chocolatine-2-4B-Instruct-DPO-v2.1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="jpacifico/Chocolatine-2-4B-Instruct-DPO-v2.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("jpacifico/Chocolatine-2-4B-Instruct-DPO-v2.1")
model = AutoModelForMultimodalLM.from_pretrained("jpacifico/Chocolatine-2-4B-Instruct-DPO-v2.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jpacifico/Chocolatine-2-4B-Instruct-DPO-v2.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jpacifico/Chocolatine-2-4B-Instruct-DPO-v2.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jpacifico/Chocolatine-2-4B-Instruct-DPO-v2.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jpacifico/Chocolatine-2-4B-Instruct-DPO-v2.1

SGLang

How to use jpacifico/Chocolatine-2-4B-Instruct-DPO-v2.1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "jpacifico/Chocolatine-2-4B-Instruct-DPO-v2.1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jpacifico/Chocolatine-2-4B-Instruct-DPO-v2.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "jpacifico/Chocolatine-2-4B-Instruct-DPO-v2.1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jpacifico/Chocolatine-2-4B-Instruct-DPO-v2.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jpacifico/Chocolatine-2-4B-Instruct-DPO-v2.1 with Docker Model Runner:
```
docker model run hf.co/jpacifico/Chocolatine-2-4B-Instruct-DPO-v2.1
```

jpacifico commited on Mar 25

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5eecaaf

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@@ -35,11 +35,17 @@ Optimized variants (MLX, GGUF) are also available, making the model particularly
 - **Post training methods:** DPO + Model Merging
 - **Objective:** Improve French instruction-following and reasoning through post-training, while maintaining strong multilingual performance and keeping the same model size.
 ## Benchmarks
 Performance improves consistently across all tested FR benchmarks :
-| Benchmark | Qwen/Qwen3-4B-Thinking-2507 | Chocolatine-2-4B-Instruct-DPO-v2.1 |
 |---|---:|---:|
 | french_bench_arc_challenge | 47.13 | **49.79** |
 | french_bench_grammar | 70.59 | **72.27** |

 - **Post training methods:** DPO + Model Merging
 - **Objective:** Improve French instruction-following and reasoning through post-training, while maintaining strong multilingual performance and keeping the same model size.
+**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)
+- 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)
 ## Benchmarks
 Performance improves consistently across all tested FR benchmarks :
+| Benchmark | Qwen3-4B-Thinking-2507 | Chocolatine-2-4B-Instruct-DPO-v2.1 |
 |---|---:|---:|
 | french_bench_arc_challenge | 47.13 | **49.79** |
 | french_bench_grammar | 70.59 | **72.27** |