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Browse files- README.md +38 -1
- config.json +3 -4
- llm.mnn +3 -0
- llm.mnn.json +3 -0
- llm.mnn.weight +3 -0
- llm_config.json +3 -4
README.md
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# chatglm2-6b-MNN
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## Introduction
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# chatglm2-6b-MNN
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## Introduction
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This model is a 4-bit quantized version of the MNN model exported from [chatglm2-6b](https://modelscope.cn/models/ZhipuAI/chatglm2-6b/summary) using [llmexport](https://github.com/alibaba/MNN/tree/master/transformers/llm/export).
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## Download
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```bash
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# install huggingface
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pip install huggingface
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```
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```bash
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# shell download
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huggingface download --model 'taobao-mnn/chatglm2-6b-MNN' --local_dir 'path/to/dir'
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```
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```python
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# SDK download
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from huggingface_hub import snapshot_download
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model_dir = snapshot_download('taobao-mnn/chatglm2-6b-MNN')
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```
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```bash
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# git clone
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git clone https://www.modelscope.cn/taobao-mnn/chatglm2-6b-MNN
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```
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## Usage
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```bash
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# clone MNN source
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git clone https://github.com/alibaba/MNN.git
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# compile
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cd MNN
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mkdir build && cd build
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cmake .. -DMNN_LOW_MEMORY=true -DMNN_CPU_WEIGHT_DEQUANT_GEMM=true -DMNN_BUILD_LLM=true -DMNN_SUPPORT_TRANSFORMER_FUSE=true
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make -j
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# run
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./llm_demo /path/to/chatglm2-6b-MNN/config.json prompt.txt
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```
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## Document
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[MNN-LLM](https://mnn-docs.readthedocs.io/en/latest/transformers/llm.html#)
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config.json
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{
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"llm_model": "
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"llm_weight": "
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"backend_type": "cpu",
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"thread_num": 4,
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"precision": "low",
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"memory": "low"
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}
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{
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"llm_model": "llm.mnn",
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"llm_weight": "llm.mnn.weight",
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"backend_type": "cpu",
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"thread_num": 4,
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"precision": "low",
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"memory": "low"
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}
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llm.mnn
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version https://git-lfs.github.com/spec/v1
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oid sha256:5c3deb1d93a58fdefb9a2b5752ecedbedb095bbfb5fa80cf715a1e71c2456af6
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size 2722368
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llm.mnn.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:1fd14bee2a4a93615d519bf607ccd87f22a6d0f45c4dcd883b8844224c39446a
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size 16353637
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llm.mnn.weight
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version https://git-lfs.github.com/spec/v1
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oid sha256:3a56e478747e18bba08d260d3dcc19f6073eb0a68445a0364da4ddb595ede5c5
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size 3362516614
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llm_config.json
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{
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"hidden_size": 4096,
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"layer_nums": 28,
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"attention_mask": "
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"key_value_shape": [
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"prompt_template": "[Round 1]\n\n问:%s\n\n答:",
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"is_visual": false
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"is_single": true
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}
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{
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"hidden_size": 4096,
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"layer_nums": 28,
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"attention_mask": "float",
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"key_value_shape": [
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"prompt_template": "[Round 1]\n\n问:%s\n\n答:",
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"is_visual": false
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}
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