Instructions to use fireworks-ai/mistral-7b-eagle-head-experimental with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fireworks-ai/mistral-7b-eagle-head-experimental with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="fireworks-ai/mistral-7b-eagle-head-experimental")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("fireworks-ai/mistral-7b-eagle-head-experimental") model = AutoModelForCausalLM.from_pretrained("fireworks-ai/mistral-7b-eagle-head-experimental", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use fireworks-ai/mistral-7b-eagle-head-experimental with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "fireworks-ai/mistral-7b-eagle-head-experimental" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "fireworks-ai/mistral-7b-eagle-head-experimental", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/fireworks-ai/mistral-7b-eagle-head-experimental
- SGLang
How to use fireworks-ai/mistral-7b-eagle-head-experimental 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 "fireworks-ai/mistral-7b-eagle-head-experimental" \ --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": "fireworks-ai/mistral-7b-eagle-head-experimental", "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 "fireworks-ai/mistral-7b-eagle-head-experimental" \ --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": "fireworks-ai/mistral-7b-eagle-head-experimental", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use fireworks-ai/mistral-7b-eagle-head-experimental with Docker Model Runner:
docker model run hf.co/fireworks-ai/mistral-7b-eagle-head-experimental
Download pytorch_model.bin from fireworks-ai/mistral-7b-eagle-head-experimental: direct link, hf CLI and curl.
- Browser
- Download file 1.63 GB
-
https://huggingface.co/fireworks-ai/mistral-7b-eagle-head-experimental/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://fireworks-ai/mistral-7b-eagle-head-experimental/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/fireworks-ai/mistral-7b-eagle-head-experimental/resolve/main/pytorch_model.bin
1.63 GB
- Xet hash:
- aad7a8a844a4756706f0f4b0b1584b6bb0143a8861baf18ff749239698b55e35
- Size of remote file:
- 1.63 GB
- SHA256:
- eafc888c051d846f86409bdbc55bc9073856621e08f8cb23f1f3896d083017cd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.