Instructions to use QuantFactory/Chocolatine-3B-Instruct-DPO-v1.2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Chocolatine-3B-Instruct-DPO-v1.2-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="QuantFactory/Chocolatine-3B-Instruct-DPO-v1.2-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Chocolatine-3B-Instruct-DPO-v1.2-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/Chocolatine-3B-Instruct-DPO-v1.2-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Chocolatine-3B-Instruct-DPO-v1.2-GGUF",
	filename="Chocolatine-3B-Instruct-DPO-v1.2.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/Chocolatine-3B-Instruct-DPO-v1.2-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantFactory/Chocolatine-3B-Instruct-DPO-v1.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Chocolatine-3B-Instruct-DPO-v1.2-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantFactory/Chocolatine-3B-Instruct-DPO-v1.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Chocolatine-3B-Instruct-DPO-v1.2-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf QuantFactory/Chocolatine-3B-Instruct-DPO-v1.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Chocolatine-3B-Instruct-DPO-v1.2-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf QuantFactory/Chocolatine-3B-Instruct-DPO-v1.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Chocolatine-3B-Instruct-DPO-v1.2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Chocolatine-3B-Instruct-DPO-v1.2-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Chocolatine-3B-Instruct-DPO-v1.2-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Chocolatine-3B-Instruct-DPO-v1.2-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Chocolatine-3B-Instruct-DPO-v1.2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Chocolatine-3B-Instruct-DPO-v1.2-GGUF:Q4_K_M

SGLang

How to use QuantFactory/Chocolatine-3B-Instruct-DPO-v1.2-GGUF 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 "QuantFactory/Chocolatine-3B-Instruct-DPO-v1.2-GGUF" \
    --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": "QuantFactory/Chocolatine-3B-Instruct-DPO-v1.2-GGUF",
		"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 "QuantFactory/Chocolatine-3B-Instruct-DPO-v1.2-GGUF" \
        --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": "QuantFactory/Chocolatine-3B-Instruct-DPO-v1.2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantFactory/Chocolatine-3B-Instruct-DPO-v1.2-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Chocolatine-3B-Instruct-DPO-v1.2-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/Chocolatine-3B-Instruct-DPO-v1.2-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/Chocolatine-3B-Instruct-DPO-v1.2-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/Chocolatine-3B-Instruct-DPO-v1.2-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Chocolatine-3B-Instruct-DPO-v1.2-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use QuantFactory/Chocolatine-3B-Instruct-DPO-v1.2-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Chocolatine-3B-Instruct-DPO-v1.2-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Chocolatine-3B-Instruct-DPO-v1.2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Chocolatine-3B-Instruct-DPO-v1.2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Chocolatine-3B-Instruct-DPO-v1.2-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/Chocolatine-3B-Instruct-DPO-v1.2-GGUF

This is quantized version of jpacifico/Chocolatine-3B-Instruct-DPO-v1.2 created using llama.cpp

Original Model Card

Chocolatine-3B-Instruct-DPO-v1.2

DPO fine-tuned of microsoft/Phi-3.5-mini-instruct (3.82B 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.
The model supports 128K context length.

OpenLLM Leaderboard

TBD.

MT-Bench-French

Chocolatine-3B-Instruct-DPO-v1.2 is outperforming Phi-3-medium-4k-instruct (14B) and its base model Phi-3.5-mini-instruct on MT-Bench-French, used with multilingual-mt-bench and GPT-4-Turbo as LLM-judge.

########## First turn ##########
                                             score
model                                 turn        
gpt-4o-mini                           1     9.2875
Chocolatine-14B-Instruct-4k-DPO       1     8.6375
Chocolatine-14B-Instruct-DPO-v1.2     1     8.6125
Phi-3.5-mini-instruct                 1     8.5250
Chocolatine-3B-Instruct-DPO-v1.2      1     8.3750
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
Meta-Llama-3.1-8B-Instruct            1     7.0500
vigostral-7b-chat                     1     6.7875
Mistral-7B-Instruct-v0.3              1     6.7500
gemma-2-2b-it                         1     6.4500
French-Alpaca-7B-Instruct_beta        1     5.6875
vigogne-2-7b-chat                     1     5.6625

########## Second turn ##########
                                               score
model                                 turn          
gpt-4o-mini                           2     8.912500
Chocolatine-14B-Instruct-DPO-v1.2     2     8.337500
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
Daredevil-8B                          2     7.087500
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
French-Alpaca-7B-Instruct_beta        2     5.487395
vigogne-2-7b-chat                     2     2.775000

########## Average ##########
                                          score
model                                          
gpt-4o-mini                            9.100000
Chocolatine-14B-Instruct-DPO-v1.2      8.475000
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
Daredevil-8B                           7.487500
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
French-Alpaca-7B-Instruct_beta         5.587866
vigogne-2-7b-chat                      4.218750

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'])

4-bit quantized version is available here : jpacifico/Chocolatine-3B-Instruct-DPO-v1.2-Q4_K_M-GGUF

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.

Developed by: Jonathan Pacifico, 2024
Model type: LLM
Language(s) (NLP): French, English
License: MIT

Downloads last month: 258

GGUF

Model size

4B params

Architecture

phi3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

QuantFactory
/

Chocolatine-3B-Instruct-DPO-v1.2-GGUF