模型:
TheBloke/wizardLM-7B-GPTQ
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这些文件是用于 WizardLM's WizardLM-7B 4bit 的GPTQ模型文件。
提供了多个不同的GPTQ参数排列,有关提供选项、参数以及创建它们所使用的软件的详细信息,请参阅下面的提供的文件部分。
这些模型是使用 Latitude.sh 提供的硬件进行量化的。
{prompt} ### Response:
提供了多个量化参数,以便您可以选择适合您的硬件和要求的最佳选项。
每个不同的量化在不同的分支中。请参见下面的获取不同分支的说明。
Branch | Bits | Group Size | Act Order (desc_act) | File Size | ExLlama Compatible? | Made With | Description |
---|---|---|---|---|---|---|---|
main | 4 | 128 | False | 4.52 GB | True | GPTQ-for-LLaMa | Most compatible option. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. Lower inference quality than other options. |
gptq-4bit-32g-actorder_True | 4 | 32 | True | 4.28 GB | True | AutoGPTQ | 4-bit, with Act Order and group size. 32g gives highest possible inference quality, with maximum VRAM usage. Poor AutoGPTQ CUDA speed. |
gptq-4bit-64g-actorder_True | 4 | 64 | True | 4.02 GB | True | AutoGPTQ | 4-bit, with Act Order and group size. 64g uses less VRAM than 32g, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. |
gptq-4bit-128g-actorder_True | 4 | 128 | True | 3.90 GB | True | AutoGPTQ | 4-bit, with Act Order and group size. 128g uses even less VRAM, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. |
gptq-8bit--1g-actorder_True | 8 | None | True | 7.01 GB | False | AutoGPTQ | 8-bit, with Act Order. No group size, to lower VRAM requirements and to improve AutoGPTQ speed. |
gptq-8bit-128g-actorder_False | 8 | 128 | False | 7.16 GB | False | AutoGPTQ | 8-bit, with group size 128g for higher inference quality and without Act Order to improve AutoGPTQ speed. |
gptq-8bit-128g-actorder_True | 8 | 128 | True | 7.16 GB | False | AutoGPTQ | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. Poor AutoGPTQ CUDA speed. |
gptq-8bit-64g-actorder_True | 8 | 64 | True | 7.31 GB | False | AutoGPTQ | 8-bit, with group size 64g and Act Order for maximum inference quality. Poor AutoGPTQ CUDA speed. |
git clone --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/wizardLM-7B-GPTQ`
请确保您正在使用最新版本的 text-generation-webui 。
强烈建议使用文本生成webui的一键安装程序,除非您知道如何进行手动安装。
首先确保您安装了 AutoGPTQ :
GITHUB_ACTIONS=true pip install auto-gptq
然后尝试以下示例代码:
from transformers import AutoTokenizer, pipeline, logging from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig model_name_or_path = "TheBloke/wizardLM-7B-GPTQ" model_basename = "wizardLM-7B-GPTQ-4bit-128g.no-act.order" use_triton = False tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) model = AutoGPTQForCausalLM.from_quantized(model_name_or_path, model_basename=model_basename use_safetensors=True, trust_remote_code=True, device="cuda:0", use_triton=use_triton, quantize_config=None) """ To download from a specific branch, use the revision parameter, as in this example: model = AutoGPTQForCausalLM.from_quantized(model_name_or_path, revision="gptq-4bit-32g-actorder_True", model_basename=model_basename, use_safetensors=True, trust_remote_code=True, device="cuda:0", quantize_config=None) """ prompt = "Tell me about AI" prompt_template=f'''{prompt} ### Response: ''' print("\n\n*** Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline # Prevent printing spurious transformers error when using pipeline with AutoGPTQ logging.set_verbosity(logging.CRITICAL) print("*** Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, temperature=0.7, top_p=0.95, repetition_penalty=1.15 ) print(pipe(prompt_template)[0]['generated_text'])
提供的文件可与AutoGPTQ(CUDA和Triton模式)、GPTQ-for-LLaMa(仅CUDA已经过测试)和Occ4m的GPTQ-for-LLaMa分支一起使用。
ExLlama与4位Llama模型兼容。有关每个文件的兼容性,请参见上面提供的文件表。
如需进一步支持以及有关这些模型和AI的讨论,请加入我们的群组:
感谢 chirper.ai -团队!
很多人问我是否可以做出贡献。我喜欢提供模型和帮助人们,并希望能够花更多的时间来做这些,并扩展到新的项目,如微调/训练。
如果您有能力和意愿进行贡献,我将非常感激,并将帮助我继续提供更多模型,并开始进行新的AI项目。
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特别感谢:CarbonQuill的Luke,Aemon Algiz。
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