初始化项目，由ModelHub XC社区提供模型
Model: dahara1/llama3.1-8b-Instruct-awq Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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- ja
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---
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llama3.1-8bのAWQ量子化版です。
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4GB超のGPUメモリがあれば高速に動かす事ができます。
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This is the AWQ quantization version of llama3.1-8b.
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If you have more than 4GB of GPU memory, you can run it at high speed.
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量子化時に日本語と中国語を多めに使っているため、[hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4](https://huggingface.co/hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4)より日本語データを使って計測したPerplexityが良い事がわかっています
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Because Japanese and Chinese are used a lot during quantization, It is known that Perplexity measured using Japanese data is better than [hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4](https://huggingface.co/hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4).
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## セットアップ(setup)
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```
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pip install transformers==4.43.3 autoawq==0.2.6 accelerate==0.33.0
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```
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## サンプルスクリプト(sample script)
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```
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, AwqConfig
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model_id = "dahara1/llama3.1-8b-Instruct-awq"
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quantization_config = AwqConfig(
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bits=4,
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fuse_max_seq_len=512, # Note: Update this as per your use-case
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do_fuse=True,
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)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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low_cpu_mem_usage=True,
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device_map="auto",
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quantization_config=quantization_config
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)
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prompt = [
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{"role": "system", "content": "あなたは親切で役に立つアシスタントです。常に海賊のように返答してください"},
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{"role": "user", "content": "ディープラーニングとは何ですか？"},
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]
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inputs = tokenizer.apply_chat_template(
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prompt,
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tokenize=True,
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add_generation_prompt=True,
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return_tensors="pt",
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return_dict=True,
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).to("cuda")
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outputs = model.generate(**inputs, do_sample=True, max_new_tokens=256)
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print(tokenizer.batch_decode(outputs[:, inputs['input_ids'].shape[1]:], skip_special_tokens=True)[0])
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```
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config.json
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config.json
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{
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"_name_or_path": "/root/.cache/huggingface/hub/models--meta-llama--Meta-Llama-3.1-8B-Instruct/snapshots/07eb05b21d191a58c577b4a45982fe0c049d0693",
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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": 128000,
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"eos_token_id": [
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128001,
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128008,
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128009
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],
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 131072,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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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-05,
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"rope_scaling": {
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"factor": 8.0,
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"high_freq_factor": 4.0,
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"low_freq_factor": 1.0,
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"original_max_position_embeddings": 8192,
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"rope_type": "llama3"
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},
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"rope_theta": 500000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.43.2",
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"use_cache": true,
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"vocab_size": 128256
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}
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generation_config.json
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"temperature": 0.6,
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"top_p": 0.9,
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"transformers_version": "4.43.2"
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}
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"model.layers.30.mlp.up_proj.qweight": "model-00001-of-00002.safetensors",
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|
||||
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|
||||
"model.norm.weight": "model-00001-of-00002.safetensors",
|
||||
"lm_head.weight": "model-00002-of-00002.safetensors"
|
||||
}
|
||||
}
|
||||
16
special_tokens_map.json
Normal file
16
special_tokens_map.json
Normal file
@@ -0,0 +1,16 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|eot_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
410563
tokenizer.json
Normal file
410563
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
2062
tokenizer_config.json
Normal file
2062
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user