Text Generation
Transformers
Safetensors
deepseek_v3
conversational
custom_code
text-generation-inference
fp8
Instructions to use Alphatao/Affine-0000000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Alphatao/Affine-0000000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Alphatao/Affine-0000000", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Alphatao/Affine-0000000", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("Alphatao/Affine-0000000", trust_remote_code=True, device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Alphatao/Affine-0000000 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Alphatao/Affine-0000000" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Alphatao/Affine-0000000", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Alphatao/Affine-0000000
- SGLang
How to use Alphatao/Affine-0000000 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 "Alphatao/Affine-0000000" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Alphatao/Affine-0000000", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "Alphatao/Affine-0000000" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Alphatao/Affine-0000000", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Alphatao/Affine-0000000 with Docker Model Runner:
docker model run hf.co/Alphatao/Affine-0000000
Download model-00032-of-000163.safetensors from Alphatao/Affine-0000000: direct link, hf CLI and curl.
- Browser
- Download file 4.3 GB
-
https://huggingface.co/Alphatao/Affine-0000000/resolve/d668f01bd3c2be1d2da2e43d979b5e80bae994de/model-00032-of-000163.safetensors
- Command line
-
hf download hf://Alphatao/Affine-0000000@d668f01bd3c2be1d2da2e43d979b5e80bae994de/model-00032-of-000163.safetensors
-
curl -L -o model-00032-of-000163.safetensors https://huggingface.co/Alphatao/Affine-0000000/resolve/d668f01bd3c2be1d2da2e43d979b5e80bae994de/model-00032-of-000163.safetensors
4.3 GB
- Xet hash:
- 08ebf9f8728e612ba62cc9a55ea8913ef143f74a5c674a09e4277b1352075d0a
- Size of remote file:
- 4.3 GB
- SHA256:
- f9a9a8578c7836965c34933452771d729ebe5834b69127192a7b6e57db8bb4d1
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