Instructions to use jpacifico/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jpacifico/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="jpacifico/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jpacifico/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1")
model = AutoModelForCausalLM.from_pretrained("jpacifico/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jpacifico/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jpacifico/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1"
# 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-Cook-3B-combined-SFT-DPO-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jpacifico/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1

SGLang

How to use jpacifico/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1 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-Cook-3B-combined-SFT-DPO-v0.1" \
    --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-Cook-3B-combined-SFT-DPO-v0.1",
		"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-Cook-3B-combined-SFT-DPO-v0.1" \
        --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-Cook-3B-combined-SFT-DPO-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jpacifico/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1 with Docker Model Runner:
```
docker model run hf.co/jpacifico/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Description model

Chocolatine-3B version specialized in French culinary language, fine-tuning of microsoft/Phi-3.5-mini-instruct.
This model is based on 283 specific terms and definitions of French cuisine.

Fine Tuning

For this version of the model I experimented a training method with a double fine-tuning, SFT then DPO.
I generated two datasets exclusively for this model, with GPT-4o deployed on Azure OpenAI.
The challenge was to achieve a consistent alignment between the two fine-tuning methods.
SFT to teach the terms and DPO to reinforce the understanding achieved during the first learning.

Fine tuning done efficiently with Unsloth, with which I saved processing time on a single T4 GPU (AzureML compute instance).

Usage

The recommended usage is by loading the low-rank adapter using unsloth:

from unsloth import FastLanguageModel
from transformers import TextStreamer
import torch

model_name = "jpacifico/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1"

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name,
    max_seq_length=2048,
    dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
    load_in_4bit=False
)

FastLanguageModel.for_inference(model)
model.eval()

def generate_response(user_question: str):
    messages = [
        {"role": "system", "content": "Tu es un assistant IA spécialisé dans le langage culinaire français. Une question te sera posée. Tu dois générer une réponse précise et concise."},
        {"role": "user", "content": "En cuisine "+user_question},
    ]

    inputs = tokenizer.apply_chat_template(
        messages,
        tokenize=True,
        add_generation_prompt=True,
        return_tensors="pt",
    ).to("cuda")

    attention_mask = (inputs != tokenizer.pad_token_id).long()

    text_streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

    with torch.no_grad():
        _ = model.generate(
            input_ids=inputs,
            attention_mask=attention_mask,
            max_new_tokens=128,
            use_cache=True,
            streamer=text_streamer,
            do_sample=False,
            temperature=0.7,
        )

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.

Developed by: Jonathan Pacifico, 2024
License: MIT
Finetuned from model : microsoft/Phi-3.5-mini-instruct

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Safetensors

Model size

4B params

Tensor type

F16

Model tree for jpacifico/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1

Quantizations

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jpacifico
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Chocolatine-Cook-3B-combined-SFT-DPO-v0.1

Description model

Fine Tuning

Usage

Limitations

Model tree for jpacifico/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1

Datasets used to train jpacifico/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1