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 Apr 7

Commit

4b4b720

verified ·

1 Parent(s): 052dd5e

Update README.md

Browse files

Files changed (1) hide show

README.md +2 -0

README.md CHANGED Viewed

@@ -44,6 +44,8 @@ For use cases requiring explicit reasoning traces or structured thinking outputs
 - 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)
 ## 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.

 - 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.