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license: apache-2.0 |
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# THUDM's chatglm2 6B GGML |
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These files are GGML format model files for [THUDM's chatglm2 6B](https://huggingface.co/THUDM/chatglm2-6b). |
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GGML files are for CPU + GPU inference using [chatglm.cpp](https://github.com/li-plus/chatglm.cpp) and [Xorbits Inference](https://github.com/xorbitsai/inference). |
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# Prompt template |
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**NOTE**: prompt template is not available yet since the system prompt is hard coded in chatglm.cpp for now. |
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# Provided files |
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| Name | Quant method | Bits | Size | |
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|------|--------------|------|------| |
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| chatglm2-ggml-q4_0.bin | q4_0 | 4 | 3.5 GB | |
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| chatglm2-ggml-q4_1.bin | q4_1 | 4 | 3.9 GB | |
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| chatglm2-ggml-q5_0.bin | q5_0 | 5 | 4.3 GB | |
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| chatglm2-ggml-q5_1.bin | q5_1 | 5 | 4.7 GB | |
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| chatglm2-ggml-q8_0.bin | q8_0 | 8 | 6.6 GB | |
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# How to run in Xorbits Inference |
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## Install |
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Xinference can be installed via pip from PyPI. It is highly recommended to create a new virtual environment to avoid conflicts. |
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```bash |
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$ pip install "xinference[all]" |
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$ pip install chatglm-cpp |
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``` |
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## Start Xorbits Inference |
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To start a local instance of Xinference, run the following command: |
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```bash |
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$ xinference |
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``` |
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Once Xinference is running, an endpoint will be accessible for model management via CLI or Xinference client. The default endpoint is `http://localhost:9997`. You can also view a web UI using the Xinference endpoint to chat with all the builtin models. You can even chat with two cutting-edge AI models side-by-side to compare their performance! |
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# Slack |
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For further support, and discussions on these models and AI in general, join our [slack channel](https://join.slack.com/t/xorbitsio/shared_invite/zt-1o3z9ucdh-RbfhbPVpx7prOVdM1CAuxg)! |
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# Original model card: THUDM's chatglm2 6B |
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ChatGLM**2**-6B is the second-generation version of the open-source bilingual (Chinese-English) chat model [ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B). It retains the smooth conversation flow and low deployment threshold of the first-generation model, while introducing the following new features: |
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1. **Stronger Performance**: Based on the development experience of the first-generation ChatGLM model, we have fully upgraded the base model of ChatGLM2-6B. ChatGLM2-6B uses the hybrid objective function of [GLM](https://github.com/THUDM/GLM), and has undergone pre-training with 1.4T bilingual tokens and human preference alignment training. The [evaluation results](README.md#evaluation-results) show that, compared to the first-generation model, ChatGLM2-6B has achieved substantial improvements in performance on datasets like MMLU (+23%), CEval (+33%), GSM8K (+571%), BBH (+60%), showing strong competitiveness among models of the same size. |
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2. **Longer Context**: Based on [FlashAttention](https://github.com/HazyResearch/flash-attention) technique, we have extended the context length of the base model from 2K in ChatGLM-6B to 32K, and trained with a context length of 8K during the dialogue alignment, allowing for more rounds of dialogue. However, the current version of ChatGLM2-6B has limited understanding of single-round ultra-long documents, which we will focus on optimizing in future iterations. |
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3. **More Efficient Inference**: Based on [Multi-Query Attention](http://arxiv.org/abs/1911.02150) technique, ChatGLM2-6B has more efficient inference speed and lower GPU memory usage: under the official implementation, the inference speed has increased by 42% compared to the first generation; under INT4 quantization, the dialogue length supported by 6G GPU memory has increased from 1K to 8K. |
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For more instructions, including how to run CLI and web demos, and model quantization, please refer to our [Github Repo](https://github.com/THUDM/ChatGLM2-6B). |
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