Text Generation
Transformers
Safetensors
French
English
binaryllm
binary-level
bit-level
causal-lm
tokenizer-free
base2
binary
TinyTransformerLM
custom_code
Instructions to use PhysiQuanty/Binary-LLM-POC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PhysiQuanty/Binary-LLM-POC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="PhysiQuanty/Binary-LLM-POC", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("PhysiQuanty/Binary-LLM-POC", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use PhysiQuanty/Binary-LLM-POC with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "PhysiQuanty/Binary-LLM-POC" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "PhysiQuanty/Binary-LLM-POC", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/PhysiQuanty/Binary-LLM-POC
- SGLang
How to use PhysiQuanty/Binary-LLM-POC 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 "PhysiQuanty/Binary-LLM-POC" \ --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": "PhysiQuanty/Binary-LLM-POC", "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 "PhysiQuanty/Binary-LLM-POC" \ --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": "PhysiQuanty/Binary-LLM-POC", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use PhysiQuanty/Binary-LLM-POC with Docker Model Runner:
docker model run hf.co/PhysiQuanty/Binary-LLM-POC
Update inference.py
Browse files- inference.py +1 -1
inference.py
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@@ -198,7 +198,7 @@ def main() -> None:
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with torch.no_grad():
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for _ in range(int(args.max_new_tokens)):
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# full forward sur toute la séquence, sans cache
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out = m(input_ids=tokens, use_cache=
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logits = out.logits[:, -1, :]
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full_seq = tokens[0].tolist()
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with torch.no_grad():
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for _ in range(int(args.max_new_tokens)):
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# full forward sur toute la séquence, sans cache
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out = m(input_ids=tokens, use_cache=True)
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logits = out.logits[:, -1, :]
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full_seq = tokens[0].tolist()
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