hurongliang
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init
Browse files- README.md +34 -0
- config.json +19 -0
- tokenizer.model +3 -0
- weights.0.npz +3 -0
- weights.1.npz +3 -0
- weights.2.npz +3 -0
- weights.3.npz +3 -0
- weights.4.npz +3 -0
- weights.5.npz +3 -0
- weights.6.npz +3 -0
- weights.7.npz +3 -0
README.md
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---
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license: apache-2.0
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---
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---
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license: apache-2.0
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inference: false
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library_name: mlx
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---
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# Model Card for Mixtral-8x7B 4 bit
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The Mixtral-8x7B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts. The Mixtral-8x7B outperforms Llama 2 70B on most benchmarks we tested.
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For full details of this model please read our [release blog post](https://mistral.ai/news/mixtral-of-experts/).
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## Instruction format
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This format must be strictly respected, otherwise the model will generate sub-optimal outputs.
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The template used to build a prompt for the Instruct model is defined as follows:
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```
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<s> [INST] Instruction [/INST] Model answer</s> [INST] Follow-up instruction [/INST]
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```
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Note that `<s>` and `</s>` are special tokens for beginning of string (BOS) and end of string (EOS) while [INST] and [/INST] are regular strings.
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## Run the model
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```bash
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# Install mlx, mlx-examples, huggingface-cli
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pip install mlx
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pip install huggingface_hub hf_transfer
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git clone https://github.com/ml-explore/mlx-examples.git
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# Download model
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export HF_HUB_ENABLE_HF_TRANSFER=1
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huggingface-cli download --local-dir Mixtral-8x7B-Instruct-v0.1-4-bit https://huggingface.co/hurongliang/Mixtral-8x7B-Instruct-v0.1-4-bit
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# Run example
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python mlx-examples/llms/mixtral/mixtral.py --model_path Mixtral-8x7B-Instruct-v0.1-4-bit
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```
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config.json
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{
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"dim": 4096,
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"n_layers": 32,
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"head_dim": 128,
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"hidden_dim": 14336,
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"n_heads": 32,
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"n_kv_heads": 8,
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"norm_eps": 1e-05,
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"vocab_size": 32000,
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"moe": {
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"num_experts_per_tok": 2,
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"num_experts": 8
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},
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"quantization": {
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"group_size": 64,
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"bits": 4
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},
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"model_type": "mixtral"
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}
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tokenizer.model
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weights.0.npz
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weights.1.npz
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weights.4.npz
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weights.6.npz
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weights.7.npz
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