模型:
TheBloke/WizardLM-33B-V1.0-Uncensored-GPTQ
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这些文件是 Eric Hartford's WizardLM 33B V1.0 Uncensored 的GPTQ模型文件。
提供了多个GPTQ参数的排列组合;有关提供的选项、它们的参数以及用于创建它们的软件的详细信息,请参阅下面的提供的文件。
这些模型是使用 Latitude.sh 提供的硬件进行量化的。
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {prompt} ASSISTANT:
提供了多个量化参数,以便您可以为硬件和需求选择最佳参数。
每个单独段都在不同的分支中。请参阅下面的说明以了解从不同分支获取的方法。
Branch | Bits | Group Size | Act Order (desc_act) | File Size | ExLlama Compatible? | Made With | Description |
---|---|---|---|---|---|---|---|
main | 4 | None | True | 16.94 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 | 19.44 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 | 18.18 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 | 17.55 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 | 32.99 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 | 33.73 GB | False | AutoGPTQ | 8-bit, with group size 128g for higher inference quality and without Act Order to improve AutoGPTQ speed. |
gptq-3bit--1g-actorder_True | 3 | None | True | 12.92 GB | False | AutoGPTQ | 3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g. |
gptq-3bit-128g-actorder_False | 3 | 128 | False | 13.51 GB | False | AutoGPTQ | 3-bit, with group size 128g but no act-order. Slightly higher VRAM requirements than 3-bit None. |
git clone --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/WizardLM-33B-V1.0-Uncensored-GPTQ`
请确保您正在使用的是 text-generation-webui 的最新版本。
强烈建议使用text-generation-webui 的一键安装程序,除非您知道如何进行手动安装。
首先确保您已安装 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-33B-V1.0-Uncensored-GPTQ" model_basename = "wizardlm-33b-v1.0-uncensored-GPTQ-4bit--1g.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=False, 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=False, device="cuda:0", quantize_config=None) """ prompt = "Tell me about AI" prompt_template=f'''A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {prompt} ASSISTANT: ''' 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。有关每个选项的兼容性,请参阅上面的提供的文件表。
有关进一步支持以及有关这些模型和AI的讨论,请加入我们:
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这些模型和AI的提供给许多人,让人们能够提供帮助,并且愿意获得更多时间提供帮助,以及扩大到新的项目,如优化/训练。
如果您有能力和意愿提供帮助,我将非常感谢,并将有助于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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这是通过使用经过筛选的数据集重新训练的 https://huggingface.co/WizardLM/WizardLM-30B-V1.0 ,旨在减少拒绝、规避和偏见。
请注意,LLaMA本身具有固有的伦理信仰,所以“真正无审查的”模型是不存在的。但是,此模型比WizardLM/WizardLM-7B-V1.0更符合规定。
向开源AI/ML社区和帮助过我的每个人致敬。
注意:无审查的模型没有防护措施。您对使用该模型的一切负有责任,就像您对使用刀具、枪支、打火机或汽车等任何危险物品负责一样。发布模型生成的任何内容都与自己发布它一样。您对您发布的内容负责,不能将模型的责任归咎于它所生成的内容,就像您不能将刀具、枪支、打火机或汽车的责任归咎于您使用它们的方式一样。
与WizardLM/WizardLM-30B-V1.0一样,此模型使用Vicuna-1.1样式的提示进行训练。
You are a helpful AI assistant. USER: <prompt> ASSISTANT:
感谢 chirper.ai 赞助了我的一部分计算!