Instructions to use jpacifico/Chocolatine-2-14B-Instruct-v2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jpacifico/Chocolatine-2-14B-Instruct-v2.0 with Transformers:

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

pipe = pipeline("text-generation", model="jpacifico/Chocolatine-2-14B-Instruct-v2.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jpacifico/Chocolatine-2-14B-Instruct-v2.0")
model = AutoModelForCausalLM.from_pretrained("jpacifico/Chocolatine-2-14B-Instruct-v2.0", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jpacifico/Chocolatine-2-14B-Instruct-v2.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jpacifico/Chocolatine-2-14B-Instruct-v2.0"
# 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-2-14B-Instruct-v2.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jpacifico/Chocolatine-2-14B-Instruct-v2.0

SGLang

How to use jpacifico/Chocolatine-2-14B-Instruct-v2.0 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-2-14B-Instruct-v2.0" \
    --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-2-14B-Instruct-v2.0",
		"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-2-14B-Instruct-v2.0" \
        --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-2-14B-Instruct-v2.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jpacifico/Chocolatine-2-14B-Instruct-v2.0 with Docker Model Runner:
```
docker model run hf.co/jpacifico/Chocolatine-2-14B-Instruct-v2.0
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Chocolatine-2-14B

DPO fine-tuning of the merged model jpacifico/Chocolatine-2-14B-Merged-base-Phi-4 (Microsoft/Phi-4 architecture, 14B params)
using the jpacifico/french-orca-dpo-pairs-revised rlhf dataset.
Training in French also improves the base model's overall capabilities.

Window context : up to 16K tokens

LLM Leaderboard FR

[Updated 2025-06-26]

BAC FR benchmark Number 1 all categories on the French Government Leaderboard LLM FR

MT-Bench-French

Chocolatine-2 outperforms its previous versions and its base architecture Phi-4 model on MT-Bench-French, used with multilingual-mt-bench and GPT-4-Turbo as a LLM-judge.
My goal was to achieve GPT-4o-mini's performance on the French language, this version equals the performance of the OpenAI model according to this benchmark

########## First turn ##########
                                             score
model                                 turn        
gpt-4o-mini                           1     9.287500
Chocolatine-2-14B-Instruct-v2.0       1     8.912500
Qwen2.5-14B-Instruct                  1     8.887500
Chocolatine-14B-Instruct-4k-DPO       1     8.637500
Chocolatine-14B-Instruct-DPO-v1.2     1     8.612500
Phi-3.5-mini-instruct                 1     8.525000
Chocolatine-3B-Instruct-DPO-v1.2      1     8.375000
DeepSeek-R1-Distill-Qwen-14B          1     8.375000
phi-4                                 1     8.300000
Phi-3-medium-4k-instruct              1     8.225000
gpt-3.5-turbo                         1     8.137500
Chocolatine-3B-Instruct-DPO-Revised   1     7.987500
Meta-Llama-3.1-8B-Instruct            1     7.050000
vigostral-7b-chat                     1     6.787500
Mistral-7B-Instruct-v0.3              1     6.750000
gemma-2-2b-it                         1     6.450000

########## Second turn ##########
                                               score
model                                 turn
Chocolatine-2-14B-Instruct-v2.0       2     9.275000         
gpt-4o-mini                           2     8.912500
Qwen2.5-14B-Instruct                  2     8.912500
Chocolatine-14B-Instruct-DPO-v1.2     2     8.337500
DeepSeek-R1-Distill-Qwen-14B          2     8.200000
phi-4                                 2     8.131250
Chocolatine-3B-Instruct-DPO-Revised   2     7.937500
Chocolatine-3B-Instruct-DPO-v1.2      2     7.862500
Phi-3-medium-4k-instruct              2     7.750000
Chocolatine-14B-Instruct-4k-DPO       2     7.737500
gpt-3.5-turbo                         2     7.679167
Phi-3.5-mini-instruct                 2     7.575000
Meta-Llama-3.1-8B-Instruct            2     6.787500
Mistral-7B-Instruct-v0.3              2     6.500000
vigostral-7b-chat                     2     6.162500
gemma-2-2b-it                         2     6.100000

########## Average ##########
                                          score
model                                          
gpt-4o-mini                            9.100000
Chocolatine-2-14B-Instruct-v2.0        9.093750
Qwen2.5-14B-Instruct                   8.900000
Chocolatine-14B-Instruct-DPO-v1.2      8.475000
DeepSeek-R1-Distill-Qwen-14B           8.287500
phi-4                                  8.215625
Chocolatine-14B-Instruct-4k-DPO        8.187500
Chocolatine-3B-Instruct-DPO-v1.2       8.118750
Phi-3.5-mini-instruct                  8.050000
Phi-3-medium-4k-instruct               7.987500
Chocolatine-3B-Instruct-DPO-Revised    7.962500
gpt-3.5-turbo                          7.908333
Meta-Llama-3.1-8B-Instruct             6.918750
Mistral-7B-Instruct-v0.3               6.625000
vigostral-7b-chat                      6.475000
gemma-2-2b-it                          6.275000

Usage

You can run this model using my Colab notebook

You can also run Chocolatine-2 using the following code:

import transformers
from transformers import AutoTokenizer

# Format prompt
message = [
    {"role": "system", "content": "You are a helpful assistant chatbot."},
    {"role": "user", "content": "What is a Large Language Model?"}
]
tokenizer = AutoTokenizer.from_pretrained(new_model)
prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)

# Create pipeline
pipeline = transformers.pipeline(
    "text-generation",
    model=new_model,
    tokenizer=tokenizer
)

# Generate text
sequences = pipeline(
    prompt,
    do_sample=True,
    temperature=0.7,
    top_p=0.9,
    num_return_sequences=1,
    max_length=200,
)
print(sequences[0]['generated_text'])

Limitations

The Chocolatine 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.