Instructions to use qubitron/LLaDA-8B-Quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use qubitron/LLaDA-8B-Quantized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="qubitron/LLaDA-8B-Quantized")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("qubitron/LLaDA-8B-Quantized", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use qubitron/LLaDA-8B-Quantized with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "qubitron/LLaDA-8B-Quantized" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "qubitron/LLaDA-8B-Quantized", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/qubitron/LLaDA-8B-Quantized
- SGLang
How to use qubitron/LLaDA-8B-Quantized 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 "qubitron/LLaDA-8B-Quantized" \ --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": "qubitron/LLaDA-8B-Quantized", "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 "qubitron/LLaDA-8B-Quantized" \ --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": "qubitron/LLaDA-8B-Quantized", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use qubitron/LLaDA-8B-Quantized with Docker Model Runner:
docker model run hf.co/qubitron/LLaDA-8B-Quantized
Update README.md
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README.md
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@@ -27,7 +27,7 @@ This repository provides two post-training quantized variants of `GSAI-ML/LLaDA-
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| File | Quantization | Size | Memory Saved | Speed (A100) |
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| `llada_int8_quantized.pt` | INT8 per-row | 8.54 GB | **47%** | **9.64 tok/s** |
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| `llada_int4_quantized.pt` | INT4 packed |
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Original model (bfloat16): 16.13 GB
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| File | Quantization | Size | Memory Saved | Speed (A100) |
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| `llada_int8_quantized.pt` | INT8 per-row | 8.54 GB | **47%** | **9.64 tok/s** |
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| `llada_int4_quantized.pt` | INT4 packed | 4.79 GB | **70%** | 3.39 tok/s |
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Original model (bfloat16): 16.13 GB
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