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

Libraries

How to use QuantFactory/Chocolatine-3B-Instruct-DPO-Revised-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-Revised-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-Revised-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/Chocolatine-3B-Instruct-DPO-Revised-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-Revised-GGUF",
	filename="Chocolatine-3B-Instruct-DPO-Revised.Q2_K.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

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

Install from brew

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

Install from WinGet (Windows)

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

Use Docker

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

LM Studio
Jan

vLLM

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

Use Docker

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

SGLang

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

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

Unsloth Studio new

How to use QuantFactory/Chocolatine-3B-Instruct-DPO-Revised-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-Revised-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-Revised-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-Revised-GGUF to start chatting

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

Lemonade

How to use QuantFactory/Chocolatine-3B-Instruct-DPO-Revised-GGUF with Lemonade:

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

QuantFactory/Chocolatine-3B-Instruct-DPO-Revised-GGUF

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

Original Model Card

Chocolatine-3B-Instruct-DPO-Revised

DPO fine-tuned of microsoft/Phi-3-mini-4k-instruct (3.82B params)
using the jpacifico/french-orca-dpo-pairs-revised rlhf dataset.
Chocolatine is a general model and can itself be finetuned to be specialized for specific use cases.
Window context = 4k tokens

Benchmarks

The best 3B model on OpenLLM Leaderboard (july 2024)
5th best < 30B params (average benchmarks).

MT-Bench-French

Chocolatine-3B-Instruct-DPO-Revised is outperforming GPT-3.5-Turbo on MT-Bench-French by Bofeng Huang,
used with multilingual-mt-bench

########## First turn ##########
                                           score
model                               turn        
gpt-3.5-turbo                       1     8.1375
Chocolatine-3B-Instruct-DPO-Revised 1     7.9875
Daredevil-8B                        1     7.8875
Daredevil-8B-abliterated            1     7.8375
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
French-Alpaca-7B-Instruct_beta      1     5.6875
vigogne-2-7b-chat                   1     5.6625
vigogne-2-7b-instruct               1     5.1375

########## Second turn ##########
                                             score
model                               turn          
Chocolatine-3B-Instruct-DPO-Revised 2     7.937500
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
Daredevil-8B-abliterated            2     6.873418
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
French-Alpaca-7B-Instruct_beta      2     5.487395
vigogne-2-7b-chat                   2     2.775000
vigogne-2-7b-instruct               2     2.240506

########## Average ##########
                                        score
model                                        
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
Daredevil-8B-abliterated             7.358491
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
French-Alpaca-7B-Instruct_beta       5.587866
vigogne-2-7b-chat                    4.218750
vigogne-2-7b-instruct                3.698113

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.

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

Downloads last month: 130

GGUF

Model size

4B params

Architecture

phi3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

QuantFactory
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Chocolatine-3B-Instruct-DPO-Revised-GGUF