Instructions to use MuntasirHossain/Orpo-Mistral-7B-v0.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MuntasirHossain/Orpo-Mistral-7B-v0.3 with Transformers:

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

pipe = pipeline("text-generation", model="MuntasirHossain/Orpo-Mistral-7B-v0.3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MuntasirHossain/Orpo-Mistral-7B-v0.3")
model = AutoModelForCausalLM.from_pretrained("MuntasirHossain/Orpo-Mistral-7B-v0.3", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MuntasirHossain/Orpo-Mistral-7B-v0.3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MuntasirHossain/Orpo-Mistral-7B-v0.3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MuntasirHossain/Orpo-Mistral-7B-v0.3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MuntasirHossain/Orpo-Mistral-7B-v0.3

SGLang

How to use MuntasirHossain/Orpo-Mistral-7B-v0.3 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 "MuntasirHossain/Orpo-Mistral-7B-v0.3" \
    --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": "MuntasirHossain/Orpo-Mistral-7B-v0.3",
		"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 "MuntasirHossain/Orpo-Mistral-7B-v0.3" \
        --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": "MuntasirHossain/Orpo-Mistral-7B-v0.3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MuntasirHossain/Orpo-Mistral-7B-v0.3 with Docker Model Runner:
```
docker model run hf.co/MuntasirHossain/Orpo-Mistral-7B-v0.3
```

Model description

This model is an ORPO fine-tuned version of the mistralai/Mistral-7B-v0.3 on 2.5k subsamples of the mlabonne/orpo-dpo-mix-40k dataset. Thanks to Maxime Labonne for providing this amazing guide on Odds Ratio Policy Optimization (ORPO). ORPO combines the traditional supervised fine-tuning and preference alignment stages into a single process.

This model follows the ChatML chat template!

How to use

import torch
from transformers import AutoTokenizer, pipeline

model_id = "MuntasirHossain/Orpo-Mistral-7B-v0.3"
tokenizer = AutoTokenizer.from_pretrained(model_id)

llm = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype=torch.float16,
    device_map="auto",
)

def generate(input_text):
  messages = [{"role": "user", "content": input_text}]
  prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
  outputs = llm(prompt, max_new_tokens=512,)
  return outputs[0]["generated_text"][len(prompt):]

generate("Explain quantum tunneling in simple terms.")

Downloads last month: 11

Safetensors

Model size

7B params

Tensor type

F16

MuntasirHossain
/

Orpo-Mistral-7B-v0.3

Model description

How to use

Dataset used to train MuntasirHossain/Orpo-Mistral-7B-v0.3