Instructions to use CausalLM/miniG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CausalLM/miniG with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="CausalLM/miniG", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("CausalLM/miniG", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use CausalLM/miniG with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CausalLM/miniG" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CausalLM/miniG", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/CausalLM/miniG
- SGLang
How to use CausalLM/miniG 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 "CausalLM/miniG" \ --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": "CausalLM/miniG", "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 "CausalLM/miniG" \ --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": "CausalLM/miniG", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use CausalLM/miniG with Docker Model Runner:
docker model run hf.co/CausalLM/miniG
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README.md
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[Text-Only Weight](https://huggingface.co/CausalLM/miniG/tree/text-only)
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[GGUF (Text-Only, not recommended)](https://huggingface.co/CausalLM/miniG/tree/gguf)
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A model trained on a synthesis dataset of over **120 million** entries, this dataset having been generated through the application of state-of-the-art language models utilizing large context windows, alongside methodologies akin to retrieval-augmented generation and knowledge graph integration, where the data synthesis is conducted within clusters derived from a curated pretraining corpus of 20 billion tokens, with subsequent validation performed by the model itself.
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[纯文本权重](https://huggingface.co/CausalLM/miniG/tree/text-only)
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[GGUF (纯文本,不推荐)](https://huggingface.co/CausalLM/miniG/tree/gguf)
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一个在超过**1.2亿**条数据合成数据集上训练的模型,这些数据集是通过应用具有大上下文窗口的最先进语言模型生成的,并结合了类似于检索增强生成和知识图谱集成的方法,数据合成是在一个由200亿个标记组成的预训练语料库中提取的聚类内进行的,随后由模型本身进行验证。
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[Text-Only Weight](https://huggingface.co/CausalLM/miniG/tree/text-only)
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[GGML with ChatGLM.cpp (recommended)](https://huggingface.co/CausalLM/miniG/tree/ggml): https://github.com/li-plus/chatglm.cpp
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[GGUF (Text-Only, not recommended)](https://huggingface.co/CausalLM/miniG/tree/gguf)
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A model trained on a synthesis dataset of over **120 million** entries, this dataset having been generated through the application of state-of-the-art language models utilizing large context windows, alongside methodologies akin to retrieval-augmented generation and knowledge graph integration, where the data synthesis is conducted within clusters derived from a curated pretraining corpus of 20 billion tokens, with subsequent validation performed by the model itself.
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[纯文本权重](https://huggingface.co/CausalLM/miniG/tree/text-only)
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[GGML 用于 ChatGLM.cpp (推荐)](https://huggingface.co/CausalLM/miniG/tree/ggml): https://github.com/li-plus/chatglm.cpp
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[GGUF (纯文本,不推荐)](https://huggingface.co/CausalLM/miniG/tree/gguf)
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一个在超过**1.2亿**条数据合成数据集上训练的模型,这些数据集是通过应用具有大上下文窗口的最先进语言模型生成的,并结合了类似于检索增强生成和知识图谱集成的方法,数据合成是在一个由200亿个标记组成的预训练语料库中提取的聚类内进行的,随后由模型本身进行验证。
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