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
Add transformers library name, pipeline tag and paper link
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by nielsr HF Staff - opened
README.md
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license: other
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license_link: LICENSE
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---
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<div align="center">
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<h1>
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Index-1.9B
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## Model Introduction
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We are excited to announce the release of a lightweight version from the Index series models: the Index-1.9B series.
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- **Index-1.9B base (this repository's model)** : The base model, with 1.9 billion non-embedding parameters, pre-trained on a 2.8T corpus mainly in Chinese and English. It leads in multiple evaluation benchmarks compared to models of the same level.
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- Index-1.9B pure : A control version of the base model with the same parameters and training strategy, but strictly filtered out all instruction-related data from the corpus to verify the impact of instructions on benchmarks.
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- Index-1.9B chat: A dialogue model aligned with SFT and DPO based on the Index-1.9B base. We found that due to the introduction of a lot of internet community corpus in our pre-training, the model has significantly more interesting chatting capabilities.
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|MPT-30B (report)|/|63.48|46.9|/|/|79.9|50.6|76.5|
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|Falcon-40B (report)|/|68.18|55.4|/|/|83.6|54.5|79.2|
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Evaluation code is based on [OpenCompass](https://github.com/open-compass/opencompass) with compatibility modifications. See the [evaluate](./evaluate/) folder for details.
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library_name: transformers
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pipeline_tag: text-generation
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---
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<div align="center">
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<h1>
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Index-1.9B
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## Model Introduction
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We are excited to announce the release of a lightweight version from the Index series models: the Index-1.9B series.
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The open-source Index-1.9B series is described in the [Index SLM Technical Report](https://huggingface.co/papers/2607.09885) and includes the following models:
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- **Index-1.9B base (this repository's model)** : The base model, with 1.9 billion non-embedding parameters, pre-trained on a 2.8T corpus mainly in Chinese and English. It leads in multiple evaluation benchmarks compared to models of the same level.
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- Index-1.9B pure : A control version of the base model with the same parameters and training strategy, but strictly filtered out all instruction-related data from the corpus to verify the impact of instructions on benchmarks.
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- Index-1.9B chat: A dialogue model aligned with SFT and DPO based on the Index-1.9B base. We found that due to the introduction of a lot of internet community corpus in our pre-training, the model has significantly more interesting chatting capabilities.
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|MPT-30B (report)|/|63.48|46.9|/|/|79.9|50.6|76.5|
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|Falcon-40B (report)|/|68.18|55.4|/|/|83.6|54.5|79.2|
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Evaluation code is based on [OpenCompass](https://github.com/open-compass/opencompass) with compatibility modifications. See the [evaluate](./evaluate/) folder for details.
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