Instructions to use DhruvParth/Mistral-7B-Instruct-v0.2-DPO-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DhruvParth/Mistral-7B-Instruct-v0.2-DPO-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="DhruvParth/Mistral-7B-Instruct-v0.2-DPO-v0.1")
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("DhruvParth/Mistral-7B-Instruct-v0.2-DPO-v0.1")
model = AutoModelForCausalLM.from_pretrained("DhruvParth/Mistral-7B-Instruct-v0.2-DPO-v0.1", 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 DhruvParth/Mistral-7B-Instruct-v0.2-DPO-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DhruvParth/Mistral-7B-Instruct-v0.2-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": "DhruvParth/Mistral-7B-Instruct-v0.2-DPO-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DhruvParth/Mistral-7B-Instruct-v0.2-DPO-v0.1

SGLang

How to use DhruvParth/Mistral-7B-Instruct-v0.2-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 "DhruvParth/Mistral-7B-Instruct-v0.2-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": "DhruvParth/Mistral-7B-Instruct-v0.2-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 "DhruvParth/Mistral-7B-Instruct-v0.2-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": "DhruvParth/Mistral-7B-Instruct-v0.2-DPO-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DhruvParth/Mistral-7B-Instruct-v0.2-DPO-v0.1 with Docker Model Runner:
```
docker model run hf.co/DhruvParth/Mistral-7B-Instruct-v0.2-DPO-v0.1
```

Model Card for DhruvParth/Mistral-7B-Instruct-v2.0-PairRM-DPO

This model is a fine-tuned version of the Mistral-7B model, utilizing Direct Preference Optimization (DPO) to better align the model's responses with human preferences, specifically in a causal language modeling context.

Model Details

Model Description

Developed by: Dhruv Parthasarathy
Model type: Fine-tuned language model
Language(s) (NLP): English
License: MIT
Finetuned from model: Mistral-7B-Instruct-v2.0

Model Sources

Repository: https://huggingface.co/DhruvParth
Paper: Direct Preference Optimization (https://arxiv.org/abs/2305.18290)
Demo: (Will soon be made available)

Uses

This model is tailored for scenarios requiring alignment with human preferences in automated responses, suitable for applications in personalized chatbots, customer support, and other interactive services.

Training Details

Notebook

The fine-tuning process and the experiments were documented in a Jupyter Notebook, available here.

Training Configuration

LoRA Configuration

LoraConfig(
    r=8,
    lora_alpha=8,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
    target_modules=['k_proj', 'v_proj', 'q_proj', 'dense']
)

BitsAndBytes Configuration

BitsAndBytesConfig(
    load_in_4bit=True,
    llm_int8_threshold=6.0,
    llm_int8_has_fp16_weight=False,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
)

Training Device Setup

device_map = {"": 0}

Training Arguments

DPOConfig(
    per_device_train_batch_size=2,
    gradient_accumulation_steps=4,
    gradient_checkpointing=True,
    learning_rate=5e-5,
    lr_scheduler_type="cosine",
    max_steps=50,
    save_strategy="no",
    logging_steps=1,
    output_dir=new_model,
    optim="paged_adamw_32bit",
    warmup_steps=5,
)

DPO Trainer Setup

DPOTrainer(
    model,
    args=training_args,
    train_dataset=updated_train_dataset,
    tokenizer=tokenizer,
    peft_config=peft_config,
    beta=0.1,
    max_prompt_length=512,
    max_length=1024,
)

Evaluation

Details on the model's performance, evaluation protocols, and results will be provided as they become available.

Citation

If you use this model or dataset, please cite it as follows:

BibTeX:

@misc{dhruvparth_mistral7b_dpo_2024,
  author = {Dhruv Parthasarathy},
  title = {Fine-tuning LLMs with Direct Preference Optimization},
  year = {2024},
  publisher = {GitHub},
  journal = {GitHub repository},
  url = {https://huggingface.co/DhruvParth/Mistral-7B-Instruct-v2.0-PairRM-DPO}
}

APA: Dhruv Parthasarathy. (2024). Fine-tuning LLMs with Direct Preference Optimization. GitHub repository, https://huggingface.co/DhruvParth/Mistral-7B-Instruct-v2.0-PairRM-DPO

For any queries or discussions regarding the project, please open an issue in the GitHub repository, post your comment in the community section, reach out to me via LinkedIn (https://www.linkedin.com/in/parthadhruv/) or contact me directly at parthasarathy.d@northeastern.edu.

Downloads last month: 8

Safetensors

Model size

7B params

Tensor type

F16

Dataset used to train DhruvParth/Mistral-7B-Instruct-v0.2-DPO-v0.1

Paper for DhruvParth/Mistral-7B-Instruct-v0.2-DPO-v0.1

Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Paper • 2305.18290 • Published May 29, 2023 • 72