Instructions to use fava-uw/fava-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fava-uw/fava-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="fava-uw/fava-model")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("fava-uw/fava-model") model = AutoModelForCausalLM.from_pretrained("fava-uw/fava-model") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use fava-uw/fava-model with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "fava-uw/fava-model" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "fava-uw/fava-model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/fava-uw/fava-model
- SGLang
How to use fava-uw/fava-model 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 "fava-uw/fava-model" \ --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": "fava-uw/fava-model", "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 "fava-uw/fava-model" \ --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": "fava-uw/fava-model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use fava-uw/fava-model with Docker Model Runner:
docker model run hf.co/fava-uw/fava-model
Added INPUT prompt (#1)
Browse files- Added INPUT prompt (9dd709109d0b53e27b38008b0ab54d033d1a125c)
Co-authored-by: Shaurya Rohatgi <shaurya0512@users.noreply.huggingface.co>
README.md
CHANGED
|
@@ -11,6 +11,9 @@ sampling_params = vllm.SamplingParams(
|
|
| 11 |
top_p=1.0,
|
| 12 |
max_tokens=1024,
|
| 13 |
)
|
|
|
|
|
|
|
|
|
|
| 14 |
output = "" # add your passage to verify
|
| 15 |
evidence = "" # add a piece of evidence
|
| 16 |
prompts = [INPUT.format_map({"evidence": evidence, "output": output})]
|
|
|
|
| 11 |
top_p=1.0,
|
| 12 |
max_tokens=1024,
|
| 13 |
)
|
| 14 |
+
|
| 15 |
+
INPUT = "Read the following references:\n{evidence}\nPlease identify all the errors in the following text using the information in the references provided and suggest edits if necessary:\n[Text] {output}\n[Edited] "
|
| 16 |
+
|
| 17 |
output = "" # add your passage to verify
|
| 18 |
evidence = "" # add a piece of evidence
|
| 19 |
prompts = [INPUT.format_map({"evidence": evidence, "output": output})]
|