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, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- 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
metadata
license: other
license_name: license
license_link: LICENSE
Index-1.9B
模型介绍
我们很高兴首次发布Index系列模型中的轻量版本:Index-1.9B系列 本次开源的Index-1.9B 系列包含以下模型:
- Index-1.9B base(本仓库模型) : 基座模型,具有 19亿 非词嵌入参数量,在2.8T 中英文为主的语料上预训练,多个评测基准上与同级别模型比处于领先.
- Index-1.9B pure : 基座模型的对照组,与base具有相同的参数和训练策略,不同之处在于我们严格过滤了该版本语料中所有指令相关的数据,以此来验证指令对benchmark的影响
- 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
- 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
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。
- Chat模型详见 Index-1.9B-Chat
- 角色扮演模型详见 Index-1.9B-Character
更多细节详见我们的GitHub和Index-1.9B技术报告
评测结果
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
| 模型 | 均分 | 英文均分 | 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 |
评测代码基于OpenCompass, 并做了适配性修改,详见evaluate代码