Instructions to use GritLM/emb_m7_sq2048_medi_bb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GritLM/emb_m7_sq2048_medi_bb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="GritLM/emb_m7_sq2048_medi_bb")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("GritLM/emb_m7_sq2048_medi_bb") model = AutoModelForCausalLM.from_pretrained("GritLM/emb_m7_sq2048_medi_bb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use GritLM/emb_m7_sq2048_medi_bb with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "GritLM/emb_m7_sq2048_medi_bb" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "GritLM/emb_m7_sq2048_medi_bb", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/GritLM/emb_m7_sq2048_medi_bb
- SGLang
How to use GritLM/emb_m7_sq2048_medi_bb 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 "GritLM/emb_m7_sq2048_medi_bb" \ --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": "GritLM/emb_m7_sq2048_medi_bb", "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 "GritLM/emb_m7_sq2048_medi_bb" \ --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": "GritLM/emb_m7_sq2048_medi_bb", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use GritLM/emb_m7_sq2048_medi_bb with Docker Model Runner:
docker model run hf.co/GritLM/emb_m7_sq2048_medi_bb
Download pytorch_model.bin from GritLM/emb_m7_sq2048_medi_bb: direct link, hf CLI and curl.
- Browser
- Download file 14.2 GB
-
https://huggingface.co/GritLM/emb_m7_sq2048_medi_bb/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://GritLM/emb_m7_sq2048_medi_bb/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/GritLM/emb_m7_sq2048_medi_bb/resolve/main/pytorch_model.bin
14.2 GB
- Xet hash:
- 4cc2d8f0a325b6f052dfdd1dd9f8cb232e8fb9f19490610fba3878df7ada4384
- Size of remote file:
- 14.2 GB
- SHA256:
- a98f17887f8d5d0056bf9610f5866d656807b4f9e77ff2f6e91e392b09abe40f
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