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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ tags:
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+ - finetuned
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+ - quantized
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+ - 4-bit
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+ - gptq
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+ - transformers
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+ - pytorch
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+ - llama
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+ - text-generation
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+ - Long Context
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+ - en
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+ - zh
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+ - dataset:THUDM/LongAlign-10k
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+ - arxiv:2401.18058
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+ - license:apache-2.0
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+ - autotrain_compatible
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+ - endpoints_compatible
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+ - text-generation-inference
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+ - region:us
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+ model_name: LongAlign-13B-64k-GPTQ
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+ base_model: THUDM/LongAlign-13B-64k
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+ inference: false
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+ model_creator: THUDM
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+ pipeline_tag: text-generation
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+ quantized_by: MaziyarPanahi
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+ ---
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+ # Description
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+ [MaziyarPanahi/LongAlign-13B-64k-GPTQ](https://huggingface.co/MaziyarPanahi/LongAlign-13B-64k-GPTQ) is a quantized (GPTQ) version of [THUDM/LongAlign-13B-64k](https://huggingface.co/THUDM/LongAlign-13B-64k)
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+
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+ ## How to use
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+ ### Install the necessary packages
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+
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+ ```
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+ pip install --upgrade accelerate auto-gptq transformers
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+ ```
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+
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+ ### Example Python code
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+
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+
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+ ```python
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+ from transformers import AutoTokenizer, pipeline
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+ from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
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+ import torch
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+
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+ model_id = "MaziyarPanahi/LongAlign-13B-64k-GPTQ"
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+
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+ quantize_config = BaseQuantizeConfig(
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+ bits=4,
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+ group_size=128,
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+ desc_act=False
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+ )
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+
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+ model = AutoGPTQForCausalLM.from_quantized(
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+ model_id,
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+ use_safetensors=True,
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+ device="cuda:0",
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+ quantize_config=quantize_config)
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+
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+ tokenizer = AutoTokenizer.from_pretrained(model_id)
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+
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+ pipe = pipeline(
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+ "text-generation",
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+ model=model,
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+ tokenizer=tokenizer,
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+ max_new_tokens=512,
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+ temperature=0.7,
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+ top_p=0.95,
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+ repetition_penalty=1.1
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+ )
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+
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+ outputs = pipe("What is a large language model?")
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+ print(outputs[0]["generated_text"])
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+ ```
config.json ADDED
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+ {
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+ "_name_or_path": "THUDM/LongAlign-13B-64k",
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+ "architectures": [
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+ "LlamaForCausalLM"
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+ ],
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+ "attention_bias": false,
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+ "attention_dropout": 0.0,
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+ "torch_dtype": "float16",
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+ "transformers_version": "4.36.2",
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+ "use_cache": true,
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+ "vocab_size": 32256
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+ }
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