Text Generation
Transformers
Safetensors
PyTorch
English
mistral
merlinite
ibm
lab
labrador
labradorite
quantized
4-bit precision
AWQ
instruct
text-generation-inference
awq
Instructions to use solidrust/merlinite-7b-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use solidrust/merlinite-7b-AWQ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="solidrust/merlinite-7b-AWQ")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("solidrust/merlinite-7b-AWQ") model = AutoModelForCausalLM.from_pretrained("solidrust/merlinite-7b-AWQ") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use solidrust/merlinite-7b-AWQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "solidrust/merlinite-7b-AWQ" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "solidrust/merlinite-7b-AWQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/solidrust/merlinite-7b-AWQ
- SGLang
How to use solidrust/merlinite-7b-AWQ 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 "solidrust/merlinite-7b-AWQ" \ --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": "solidrust/merlinite-7b-AWQ", "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 "solidrust/merlinite-7b-AWQ" \ --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": "solidrust/merlinite-7b-AWQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use solidrust/merlinite-7b-AWQ with Docker Model Runner:
docker model run hf.co/solidrust/merlinite-7b-AWQ

- Xet hash:
- 5ad7d2d11217d503b95714904a2a1d00486fd8631a512798c0578db9373272c3
- Size of remote file:
- 1.46 MB
- SHA256:
- 90af80c0a6118a165ac5df036265274363b49a9da9cc5d600dff3b9e8f409060
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.