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, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- 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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license: other
license_name: license
license_link: LICENSE
pipeline_tag: text-generation
library_name: transformers
---
<div align="center">
<h1>
Index-1.9B
</h1>
</div>
## Model Introduction
We are excited to announce the release of a lightweight version from the Index series models: the Index-1.9B series.
The open-source Index-1.9B series includes the following models:
- Index-1.9B base: 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.
- **Index-1.9B pure (this repository's model)** : 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.
- 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.
- Index-1.9B character : Introduces RAG on top of SFT and DPO to achieve few-shots role-playing customization.
**Note: This is the Base model, capable only of continuation and further training alignment, and cannot be directly interacted with.**
- For the **Chat model**, see [Index-1.9B-Chat](https://huggingface.co/IndexTeam/Index-1.9B-Chat)
- For the **Role-playing model**, see [Index-1.9B-Character](https://huggingface.co/IndexTeam/Index-1.9B-Character)
For more details, see our [GitHub](https://github.com/bilibili/Index-1.9B) and the [Index SLM Technical Report](https://huggingface.co/papers/2607.09885).
## Evaluation Results
The Index-1.9B shows excellent performance in general understanding evaluations, leading compared to recently open-sourced small models and comparable to some 7B and models larger than 10B.
|Model|Average score|Average English score|MMLU|CEVAL|CMMLU|HellaSwag|Arc-C|Arc-E|
|----|----|----|----|----|----|----|----|----|
|Google Gemma 2B|41.58|46.77|41.81|31.36|31.02|66.82|36.39|42.07|
|Phi-2 (2.7B)|58.89|**72.54**|57.61|31.12|32.05|70.94|74.51|87.1|
|Qwen1.5-1.8B|58.96|59.28|47.05|59.48|57.12|58.33|56.82|74.93|
|Qwen2-1.5B(report)|**65.17**|62.52 |56.5|70.6|70.3|66.6|43.9|83.09|
|MiniCPM-2.4B-SFT|62.53|68.75|53.8|49.19|50.97|67.29|69.44|84.48|
|**Index-1.9B-Pure**|49.55 |52.83 |43.75|42.35|43.61|63.21|42.75|61.61|
|**Index-1.9B**|**64.92** |**69.93**|52.53|57.01|52.79|80.69|65.15|81.35|
|Llama2-7B|50.79|60.31|44.32|32.42|31.11|76|46.3|74.6|
|Mistral-7B (report) |/|**69.23**|60.1|/|/|81.3|55.5|80|
|Baichuan2-7B|54.53|53.51|54.64|56.19|56.95|25.04|57.25|77.12|
|Llama2-13B|57.51|66.61|55.78|39.93|38.7|76.22|58.88|75.56|
|Baichuan2-13B|68.90|71.69|59.63|59.21|61.27|72.61|70.04|84.48|
|MPT-30B (report)|/|63.48|46.9|/|/|79.9|50.6|76.5|
|Falcon-40B (report)|/|68.18|55.4|/|/|83.6|54.5|79.2|
Evaluation code is based on [OpenCompass](https://github.com/open-compass/opencompass) with compatibility modifications. See the [evaluate](./evaluate/) folder for details. |