Instructions to use IndexTeam/Index-1.9B-Pure with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IndexTeam/Index-1.9B-Pure with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="IndexTeam/Index-1.9B-Pure", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("IndexTeam/Index-1.9B-Pure", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use IndexTeam/Index-1.9B-Pure with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "IndexTeam/Index-1.9B-Pure" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "IndexTeam/Index-1.9B-Pure", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/IndexTeam/Index-1.9B-Pure
- SGLang
How to use IndexTeam/Index-1.9B-Pure 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 "IndexTeam/Index-1.9B-Pure" \ --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": "IndexTeam/Index-1.9B-Pure", "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 "IndexTeam/Index-1.9B-Pure" \ --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": "IndexTeam/Index-1.9B-Pure", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use IndexTeam/Index-1.9B-Pure with Docker Model Runner:
docker model run hf.co/IndexTeam/Index-1.9B-Pure
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- Chat模型详见 [Index-1.9B-Chat](https://huggingface.co/IndexTeam/Index-1.9B-Chat)
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- 角色扮演模型详见 [Index-1.9B-Character](https://huggingface.co/IndexTeam/Index-1.9B-Character)
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更多细节详见我们的[GitHub](https://github.com/bilibili/Index-1.9B)和[Index-1.9B技术报告](https://github.com/bilibili/Index-1.9B/Index-1.9B
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## 评测结果
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对通用理解进行评测,Index-1.9B性能优秀,于近期开源的端侧小模型相比领先,并可以和一批7B和大于10B的模型相比较
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- Chat模型详见 [Index-1.9B-Chat](https://huggingface.co/IndexTeam/Index-1.9B-Chat)
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- 角色扮演模型详见 [Index-1.9B-Character](https://huggingface.co/IndexTeam/Index-1.9B-Character)
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更多细节详见我们的[GitHub](https://github.com/bilibili/Index-1.9B)和[Index-1.9B技术报告](https://github.com/bilibili/Index-1.9B/blob/main/Index-1.9B%20%E6%8A%80%E6%9C%AF%E6%8A%A5%E5%91%8A.pdf)
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## 评测结果
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对通用理解进行评测,Index-1.9B性能优秀,于近期开源的端侧小模型相比领先,并可以和一批7B和大于10B的模型相比较
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