Instructions to use Chayaaaaa/BioMistral_stabilityai_gamma_7b_dare_ties with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Chayaaaaa/BioMistral_stabilityai_gamma_7b_dare_ties with Transformers:

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

pipe = pipeline("text-generation", model="Chayaaaaa/BioMistral_stabilityai_gamma_7b_dare_ties")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Chayaaaaa/BioMistral_stabilityai_gamma_7b_dare_ties")
model = AutoModelForCausalLM.from_pretrained("Chayaaaaa/BioMistral_stabilityai_gamma_7b_dare_ties", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Chayaaaaa/BioMistral_stabilityai_gamma_7b_dare_ties with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Chayaaaaa/BioMistral_stabilityai_gamma_7b_dare_ties"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Chayaaaaa/BioMistral_stabilityai_gamma_7b_dare_ties",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Chayaaaaa/BioMistral_stabilityai_gamma_7b_dare_ties

SGLang

How to use Chayaaaaa/BioMistral_stabilityai_gamma_7b_dare_ties 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 "Chayaaaaa/BioMistral_stabilityai_gamma_7b_dare_ties" \
    --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": "Chayaaaaa/BioMistral_stabilityai_gamma_7b_dare_ties",
		"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 "Chayaaaaa/BioMistral_stabilityai_gamma_7b_dare_ties" \
        --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": "Chayaaaaa/BioMistral_stabilityai_gamma_7b_dare_ties",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Chayaaaaa/BioMistral_stabilityai_gamma_7b_dare_ties with Docker Model Runner:
```
docker model run hf.co/Chayaaaaa/BioMistral_stabilityai_gamma_7b_dare_ties
```

Bio_swallow_japanese

Bio_swallow_japanese is a merge of the following models using mergekit:

BioMistral/BioMistral-7B

🧩 Configuration

base_model: stabilityai/japanese-stablelm-instruct-gamma-7b
dtype: bfloat16
merge_method: dare_ties
models:
  - model: BioMistral/BioMistral-7B
    parameters:
      density: 0.74
      weight: [0, 0.2, 0.4]
parameters:
  int8_mask: true
  normalize: true

Downloads last month: 5

Safetensors

Model size

7B params

Tensor type

BF16