Instructions to use Tele-AI/TeleChat-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tele-AI/TeleChat-1B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Tele-AI/TeleChat-1B", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Tele-AI/TeleChat-1B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Tele-AI/TeleChat-1B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Tele-AI/TeleChat-1B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Tele-AI/TeleChat-1B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Tele-AI/TeleChat-1B
- SGLang
How to use Tele-AI/TeleChat-1B 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 "Tele-AI/TeleChat-1B" \ --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": "Tele-AI/TeleChat-1B", "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 "Tele-AI/TeleChat-1B" \ --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": "Tele-AI/TeleChat-1B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Tele-AI/TeleChat-1B with Docker Model Runner:
docker model run hf.co/Tele-AI/TeleChat-1B
Update README.md
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README.md
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| 12B-int8 | [TeleChat-12B-int8](https://huggingface.co/Tele-AI/TeleChat-12B-int8) |
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| 12B-int4 | [TeleChat-12B-int4](https://huggingface.co/Tele-AI/TeleChat-12B-int4) |
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**镜像下载**
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为了便于大家快速上手,我们提供了可运行的环境镜像,下载地址:[镜像下载](https://cloud.189.cn/web/share?code=vQFJRf7JBfmq) (访问码:ona6)
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# 数据开源
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### 数据介绍
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huggingface下载地址:[TeleChat-PTD](https://huggingface.co/datasets/Tele-AI/TeleChat-PTD)
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天翼云盘下载地址:[数据下载](https://cloud.189.cn/t/ia2QbaVzYf6z)(访问码:pkg8)
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# 效果评测
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TeleChat模型相比同规模模型在评测效果方面也有较好的表现,我们的评测集涵盖了包括MMLU、C-Eval、GAOKAO、AGIEval、CMMLU、 GSM8K、MATH、HumanEval、CHID等数据集,评测能力包括了自然语言理解、知识、数学计算和推理、代码生成等
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| 12B-int8 | [TeleChat-12B-int8](https://huggingface.co/Tele-AI/TeleChat-12B-int8) |
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| 12B-int4 | [TeleChat-12B-int4](https://huggingface.co/Tele-AI/TeleChat-12B-int4) |
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# 数据开源
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### 数据介绍
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huggingface下载地址:[TeleChat-PTD](https://huggingface.co/datasets/Tele-AI/TeleChat-PTD)
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# 效果评测
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TeleChat模型相比同规模模型在评测效果方面也有较好的表现,我们的评测集涵盖了包括MMLU、C-Eval、GAOKAO、AGIEval、CMMLU、 GSM8K、MATH、HumanEval、CHID等数据集,评测能力包括了自然语言理解、知识、数学计算和推理、代码生成等
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