ai-modelscope
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Browse files- .gitattributes +10 -11
- README.md +45 -3
- config.json +37 -0
- configuration.json +4 -0
- generation_config.json +7 -0
- model-00001-of-00004.safetensors +3 -0
- model-00002-of-00004.safetensors +3 -0
- model-00003-of-00004.safetensors +3 -0
- model-00004-of-00004.safetensors +3 -0
- model.safetensors.index.json +0 -0
- special_tokens_map.json +30 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +53 -0
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README.md
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---
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---
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---
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language:
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- en
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- zh
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pipeline_tag: text-generation
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tags:
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- Awq
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- int4
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- yi1.5-34B-Chat
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- pytorch
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license: Apache 2.0
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license_name: Apache 2.0
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license_link: LICENSE
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---
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## About Quantization
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我们使用modelscope [swift](https://github.com/modelscope/swift/)仓库进行AWQ 4bit量化. 量化文档可以查看[这里](https://github.com/modelscope/swift/blob/main/docs/source/LLM/LLM%E9%87%8F%E5%8C%96%E6%96%87%E6%A1%A3.md). 量化命令如下:
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We use the modelscope [swift](https://github.com/modelscope/swift/) repository to perform AWQ 4bit quantization. Quantization documentation can be found [here](https://github.com/modelscope/swift/blob/main/docs/source_en/LLM/LLM-quantization.md). The quantization command is as follows:
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```bash
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# Experimental Environment: A100
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swift export \
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--quant_bits 4 \
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--model_type yi-1_5-34b-chat \
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--quant_method awq \
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--quant_n_samples 32 \
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--dataset alpaca-zh alpaca-en sharegpt-gpt4-mini \
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--quant_seqlen 4096
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```
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Inference:
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```bash
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CUDA_VISIBLE_DEVICES=0 swift infer --model_type yi-1_5-34b-chat-awq-int4
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```
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SFT:
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```bash
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CUDA_VISIBLE_DEVICES=0 swift sft --model_type yi-1_5-34b-chat-awq-int4 --dataset leetcode-python-en
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```
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Original Model:
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[YI1.5-34B-Chat](https://modelscope.cn/models/01ai/Yi-1.5-34B-Chat/summary)
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config.json
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{
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"_name_or_path": "/mnt/workspace/.cache/modelscope/01ai/Yi-1___5-34B-Chat",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 7168,
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"initializer_range": 0.02,
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"intermediate_size": 20480,
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"max_position_embeddings": 4096,
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"model_type": "llama",
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"num_attention_heads": 56,
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"num_hidden_layers": 60,
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"num_key_value_heads": 8,
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"pad_token_id": 0,
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"pretraining_tp": 1,
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"quantization_config": {
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"bits": 4,
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"group_size": 128,
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"modules_to_not_convert": null,
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"quant_method": "awq",
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"version": "gemm",
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"zero_point": true
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},
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 5000000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.39.3",
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"use_cache": false,
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"vocab_size": 64000
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}
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configuration.json
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{
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"framework": "Pytorch",
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"task": "text-generation"
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}
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generation_config.json
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"transformers_version": "4.40.0"
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}
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model-00001-of-00004.safetensors
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model.safetensors.index.json
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special_tokens_map.json
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tokenizer.json
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tokenizer.model
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tokenizer_config.json
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}
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},
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"bos_token": "<|startoftext|>",
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"chat_template": "{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% endif %}{% if system_message is defined %}{{ system_message }}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|im_start|>user\\n' + content + '<|im_end|>\\n<|im_start|>assistant\\n' }}{% elif message['role'] == 'assistant' %}{{ content + '<|im_end|>' + '\\n' }}{% endif %}{% endfor %}",
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"use_default_system_prompt": false
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}
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