Instructions to use IndexTeam/Index-1.9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IndexTeam/Index-1.9B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="IndexTeam/Index-1.9B", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("IndexTeam/Index-1.9B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use IndexTeam/Index-1.9B 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" # 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", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/IndexTeam/Index-1.9B
- SGLang
How to use IndexTeam/Index-1.9B 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" \ --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", "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" \ --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", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use IndexTeam/Index-1.9B with Docker Model Runner:
docker model run hf.co/IndexTeam/Index-1.9B
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- Index-1.9B chat: 基于index-1.9B base通过SFT和DPO对齐后的对话模型,我们发现由于我们预训练中引入了较多互联网社区语料,聊天的趣味性明显更强
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- Index-1.9B character : 在SFT和DPO的基础上引入了RAG来实现fewshots角色扮演定制
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注意:此为Base模型,仅能续写,以及进一步的训练对齐,不能直接交互。
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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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- Index-1.9B chat: 基于index-1.9B base通过SFT和DPO对齐后的对话模型,我们发现由于我们预训练中引入了较多互联网社区语料,聊天的趣味性明显更强
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- Index-1.9B character : 在SFT和DPO的基础上引入了RAG来实现fewshots角色扮演定制
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**注意:此为Base模型,仅能续写,以及进一步的训练对齐,不能直接交互。**
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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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