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--- |
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license: cc |
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datasets: |
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- VMware/open-instruct-v1-oasst-dolly-hhrlhf |
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language: |
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- en |
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pipeline_tag: text-generation |
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inference: false |
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--- |
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# SearchUnify/xgen-7b-8k-open-instruct-gptq |
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With its industry-first robust LLM Integrations across its suite of products ([Cognitive Search](https://www.searchunify.com/products/cognitive-search/?utm_source=link&utm_medium=ml-model&utm_campaign=hugging-face), [SUVA](https://www.searchunify.com/products/suva/), [Knowbler](https://www.searchunify.com/products/knowbler/?utm_source=link&utm_medium=ml-model&utm_campaign=hugging-face), [Escalation Predictor](https://applications.searchunify.com/escalation-predictor?utm_source=link&utm_medium=ml-model&utm_campaign=hugging-face), [Agent Helper](https://applications.searchunify.com/agent-helper?utm_source=link&utm_medium=ml-model&utm_campaign=hugging-face) and [Community Helper](https://applications.searchunify.com/community-helper?utm_source=link&utm_medium=ml-model&utm_campaign=hugging-face)) coupled with the federated retrieval augmented generation (FRAG) architecture, [SearchUnify's unified cognitive platform](https://www.searchunify.com/?utm_source=link&utm_medium=ml-model&utm_campaign=hugging-face) fetches relevant information or responses to deliver more accurate and contextually appropriate support and self-service experiences. |
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Leveraging the state-of-the-art GPTQ quantization method, SearchUnify optimized the XGen-7B Model for low memory footprint and rapid response generation. |
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These are GPTQ 4bit model files for [VMWare's XGEN 7B 8K Open Instruct](https://huggingface.co/VMware/xgen-7b-8k-open-instruct). It is the result of quantizing to 4bit using GPTQ-for-LLaMa. |
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# How to use this GPTQ model from Python code |
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First, make sure you have [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) installed: |
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``` |
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pip install auto-gptq |
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``` |
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Second, install tiktoken in order to use the tokenizer |
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``` |
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pip install tiktoken |
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``` |
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``` |
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from transformers import AutoTokenizer |
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from auto_gptq import AutoGPTQForCausalLM |
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model_name_or_path = "SearchUnify-ML/xgen-7b-8k-open-instruct-gptq" |
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model_basename = "gptq_model-4bit-128g" |
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use_triton = False |
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tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, |
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use_fast=False, |
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trust_remote_code=True) |
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model = AutoGPTQForCausalLM.from_quantized(model_name_or_path, |
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model_basename=model_basename, |
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use_safetensors=False, |
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trust_remote_code=True, |
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device="cuda:0", |
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use_triton=use_triton) |
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# Note: check the prompt template is correct for this model. |
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prompt = "Explain the rules of field hockey to a novice." |
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prompt_template = f'''### Instruction: {prompt} |
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### Response:''' |
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print("\n\n*** Generate:") |
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input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() |
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output = model.generate(inputs=input_ids, temperature=0.3, max_new_tokens=512) |
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print(f"\n\n {tokenizer.decode(output[0]).split('### Response:')[1]}") |
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``` |
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