Instructions to use wjddn803/mistral-7b-tuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wjddn803/mistral-7b-tuning with Transformers:

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

pipe = pipeline("text-generation", model="wjddn803/mistral-7b-tuning")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("wjddn803/mistral-7b-tuning")
model = AutoModelForCausalLM.from_pretrained("wjddn803/mistral-7b-tuning", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use wjddn803/mistral-7b-tuning with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wjddn803/mistral-7b-tuning"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wjddn803/mistral-7b-tuning",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/wjddn803/mistral-7b-tuning

SGLang

How to use wjddn803/mistral-7b-tuning 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 "wjddn803/mistral-7b-tuning" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wjddn803/mistral-7b-tuning",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "wjddn803/mistral-7b-tuning" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wjddn803/mistral-7b-tuning",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use wjddn803/mistral-7b-tuning with Docker Model Runner:
```
docker model run hf.co/wjddn803/mistral-7b-tuning
```

Model Card for Model ID

Trained Misral 7B with ORCA-dpo-pairs, truthy-dpo, UltraFeedback

Model Details

Mistral 7B

TO USE

model = AutoModelForCausalLM.from_pretrained("wjddn803/mistral-7b-tuning", torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained("wjddn803/mistral-7b-tuning")

Model Description

**Developed by: wjddn803@korea.ac.kr

Downloads last month: 7

Safetensors

Model size

7B params

Tensor type

BF16