--- license: apache-2.0 tags: - finetuned - quantized - 4-bit - gptq - transformers - safetensors - mistral - text-generation - Safetensors - text-generation-inference - merge - 7b - mistralai/Mistral-7B-Instruct-v0.1 - HuggingFaceH4/zephyr-7b-beta - pytorch - generated_from_trainer - en - dataset:HuggingFaceH4/ultrachat_200k - dataset:HuggingFaceH4/ultrafeedback_binarized - arxiv:2305.18290 - arxiv:2310.16944 - base_model:mistralai/Mistral-7B-v0.1 - license:mit - model-index - autotrain_compatible - endpoints_compatible - has_space - region:us - license:apache-2.0 model_name: zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GPTQ base_model: MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1 inference: false model_creator: MaziyarPanahi pipeline_tag: text-generation quantized_by: MaziyarPanahi --- # Description [MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GPTQ](https://huggingface.co/MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GPTQ) is a quantized (GPTQ) version of [MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1) ## How to use ### Install the necessary packages ``` pip install --upgrade accelerate auto-gptq transformers ``` ### Example Python code ```python from transformers import AutoTokenizer, pipeline from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig import torch model_id = "MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GPTQ" quantize_config = BaseQuantizeConfig( bits=4, group_size=128, desc_act=False ) model = AutoGPTQForCausalLM.from_quantized( model_id, use_safetensors=True, device="cuda:0", quantize_config=quantize_config) tokenizer = AutoTokenizer.from_pretrained(model_id) pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, temperature=0.7, top_p=0.95, repetition_penalty=1.1 ) outputs = pipe("What is a large language model?") print(outputs[0]["generated_text"]) ```