Text Generation
Transformers
Safetensors
English
model_n_embed_16_binary_n_layer_32
feature-extraction
causal-lm
transformer
decoder-only
fixed-embeddings
binary-token-codes
research
custom_code
Instructions to use E6E831728/fixed-minimal-binary-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use E6E831728/fixed-minimal-binary-code with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="E6E831728/fixed-minimal-binary-code", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("E6E831728/fixed-minimal-binary-code", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use E6E831728/fixed-minimal-binary-code with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "E6E831728/fixed-minimal-binary-code" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "E6E831728/fixed-minimal-binary-code", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/E6E831728/fixed-minimal-binary-code
- SGLang
How to use E6E831728/fixed-minimal-binary-code 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 "E6E831728/fixed-minimal-binary-code" \ --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": "E6E831728/fixed-minimal-binary-code", "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 "E6E831728/fixed-minimal-binary-code" \ --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": "E6E831728/fixed-minimal-binary-code", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use E6E831728/fixed-minimal-binary-code with Docker Model Runner:
docker model run hf.co/E6E831728/fixed-minimal-binary-code
- Xet hash:
- 9efa3b038f463a0ad2a0e3233b28bcf99e4be267bb176e793d440db19ecb43fb
- Size of remote file:
- 1.88 GB
- SHA256:
- 9c182e1374d8fdb84520abd156d2809602cdacfe93cd8aa747c514c83c47d58e
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