Instructions to use netcat420/MFANN3bv0.17 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use netcat420/MFANN3bv0.17 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="netcat420/MFANN3bv0.17")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("netcat420/MFANN3bv0.17") model = AutoModelForCausalLM.from_pretrained("netcat420/MFANN3bv0.17") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use netcat420/MFANN3bv0.17 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "netcat420/MFANN3bv0.17" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "netcat420/MFANN3bv0.17", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/netcat420/MFANN3bv0.17
- SGLang
How to use netcat420/MFANN3bv0.17 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 "netcat420/MFANN3bv0.17" \ --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": "netcat420/MFANN3bv0.17", "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 "netcat420/MFANN3bv0.17" \ --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": "netcat420/MFANN3bv0.17", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use netcat420/MFANN3bv0.17 with Docker Model Runner:
docker model run hf.co/netcat420/MFANN3bv0.17
Config.json seems to be from one of your older models!
You better use the correct one, otherwise your quants might turn out bad, as they might use stuff from in there. Also some GUI frontends pull data from huggingface when downloading a model for the default settings of the model.
They are all the same configs as this file has not really changed. thank you for pointing that out though! I was away that night and uploaded that config file from my laptop
Np. I cross-checked with phi-2's original config and it mostly overlaps. There is only one change that made me think you reused the config from Qwen2, Yi-1.5 or another model, which was the "max_position_embeddings": 32768, in the config.json. I suppose you used a custom value, because the base model is only trained on 2048, but your finetuning dataset holds roughly up to 8k tokens of context.