模型:
TheBloke/chronos-wizardlm-uc-scot-st-13B-GPTQ
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These files are GPTQ 4bit model files for Austism's Chronos WizardLM UC Scot ST 13B .
It is the result of quantising to 4bit using GPTQ-for-LLaMa .
Please make sure you're using the latest version of text-generation-webui
First make sure you have AutoGPTQ installed:
pip install auto-gptq
Then try the following example code:
from transformers import AutoTokenizer, pipeline, logging from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig import argparse model_name_or_path = "TheBloke/chronos-wizardlm-uc-scot-st-13B-GPTQ" model_basename = "chronos-wizardlm-uc-scot-st-13B-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) 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) prompt = "Tell me about AI" prompt_template=f'''### Human: {prompt} ### Assistant:''' 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'])
chronos-wizardlm-uc-scot-st-13B-GPTQ-4bit-128g.no-act.order.safetensors
This will work with AutoGPTQ and CUDA versions of GPTQ-for-LLaMa. There are reports of issues with Triton mode of recent GPTQ-for-LLaMa. If you have issues, please use AutoGPTQ instead.
It was created with group_size 128 to increase inference accuracy, but without --act-order (desc_act) to increase compatibility and improve inference speed.
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Special thanks to : Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov.
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Thank you to all my generous patrons and donaters!
(chronos-13b+(WizardLM Uncensored+CoT+Storytelling)) 80/20 merge
intended to be much like chronos with different writing and instruction following capabilities.