Instructions to use jpacifico/Chocolatine-14B-Instruct-4k-DPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jpacifico/Chocolatine-14B-Instruct-4k-DPO with Transformers:

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

pipe = pipeline("text-generation", model="jpacifico/Chocolatine-14B-Instruct-4k-DPO", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jpacifico/Chocolatine-14B-Instruct-4k-DPO", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("jpacifico/Chocolatine-14B-Instruct-4k-DPO", trust_remote_code=True)
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jpacifico/Chocolatine-14B-Instruct-4k-DPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jpacifico/Chocolatine-14B-Instruct-4k-DPO"
# 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-14B-Instruct-4k-DPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jpacifico/Chocolatine-14B-Instruct-4k-DPO

SGLang

How to use jpacifico/Chocolatine-14B-Instruct-4k-DPO 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-14B-Instruct-4k-DPO" \
    --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-14B-Instruct-4k-DPO",
		"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-14B-Instruct-4k-DPO" \
        --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-14B-Instruct-4k-DPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jpacifico/Chocolatine-14B-Instruct-4k-DPO with Docker Model Runner:
```
docker model run hf.co/jpacifico/Chocolatine-14B-Instruct-4k-DPO
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Chocolatine-14B-Instruct-4k-DPO

DPO fine-tuned of microsoft/Phi-3-medium-4k-instruct (14B params)
using the jpacifico/french-orca-dpo-pairs-revised rlhf dataset.
Training in French also improves the model in English, surpassing the performances of its base model (MMLU).
Window context = 4k tokens

Benchmarks

Chocolatine-14B is the best-performing < 30B model in terms of MMLU-PRO on the OpenLLM Leaderboard (august 2024)

Metric	Value
Avg.	29.83
IFEval (0-Shot)	46.89
BBH (3-Shot)	48.02
MATH Lvl 5 (4-Shot)	14.88
GPQA (0-shot)	12.19
MuSR (0-shot)	15.15
MMLU-PRO (5-shot)	41.82

MT-Bench-French

Chocolatine-14B-Instruct-4k-DPO is outperforming GPT-3.5-Turbo and Phi-3-medium-4k-instruct on
MT-Bench-French, used with multilingual-mt-bench

########## First turn ##########
                                           score
model                               turn        
Chocolatine-14B-Instruct-4k-DPO     1     8.6375
Phi-3-medium-4k-instruct            1     8.2250
gpt-3.5-turbo                       1     8.1375
Chocolatine-3B-Instruct-DPO-Revised 1     7.9875
Daredevil-8B                        1     7.8875
Chocolatine-3B-Instruct-DPO-v1.0    1     7.6875
NeuralDaredevil-8B-abliterated      1     7.6250
Phi-3-mini-4k-instruct              1     7.2125
Meta-Llama-3-8B-Instruct            1     7.1625
vigostral-7b-chat                   1     6.7875
Mistral-7B-Instruct-v0.3            1     6.7500
Mistral-7B-Instruct-v0.2            1     6.2875

########## Second turn ##########
                                             score
model                               turn          
Chocolatine-3B-Instruct-DPO-Revised 2     7.937500
Phi-3-medium-4k-instruct            2     7.750000
Chocolatine-14B-Instruct-4k-DPO     2     7.737500
gpt-3.5-turbo                       2     7.679167
Chocolatine-3B-Instruct-DPO-v1.0    2     7.612500
NeuralDaredevil-8B-abliterated      2     7.125000
Daredevil-8B                        2     7.087500
Meta-Llama-3-8B-Instruct            2     6.800000
Mistral-7B-Instruct-v0.2            2     6.512500
Mistral-7B-Instruct-v0.3            2     6.500000
Phi-3-mini-4k-instruct              2     6.487500
vigostral-7b-chat                   2     6.162500

########## Average ##########
                                        score
model                                        
Chocolatine-14B-Instruct-4k-DPO      8.187500
Phi-3-medium-4k-instruct             7.987500
Chocolatine-3B-Instruct-DPO-Revised  7.962500
gpt-3.5-turbo                        7.908333
Chocolatine-3B-Instruct-DPO-v1.0     7.650000
Daredevil-8B                         7.487500
NeuralDaredevil-8B-abliterated       7.375000
Meta-Llama-3-8B-Instruct             6.981250
Phi-3-mini-4k-instruct               6.850000
Mistral-7B-Instruct-v0.3             6.625000
vigostral-7b-chat                    6.475000
Mistral-7B-Instruct-v0.2             6.400000

Usage

You can run this model using my Colab notebook

You can also run Chocolatine 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 is a quick demonstration that a base model can be easily fine-tuned to achieve compelling performance.
It does not have any moderation mechanism.