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Add default model card

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  ---
 
 
 
 
 
 
 
 
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  inference: false
 
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  ---
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  # jeiku/Average_Normie_v3.69_8B AWQ
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- ** PROCESSING .... ETA 30mins **
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-
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  - Model creator: [jeiku](https://huggingface.co/jeiku)
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  - Original model: [Average_Normie_v3.69_8B](https://huggingface.co/jeiku/Average_Normie_v3.69_8B)
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  ### About AWQ
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  AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
 
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  ---
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+ library_name: transformers
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+ tags:
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+ - 4-bit
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+ - AWQ
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+ - text-generation
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+ - autotrain_compatible
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+ - endpoints_compatible
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+ pipeline_tag: text-generation
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  inference: false
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+ quantized_by: Suparious
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  ---
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  # jeiku/Average_Normie_v3.69_8B AWQ
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  - Model creator: [jeiku](https://huggingface.co/jeiku)
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  - Original model: [Average_Normie_v3.69_8B](https://huggingface.co/jeiku/Average_Normie_v3.69_8B)
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+
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+
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+ ## How to use
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+
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+ ### Install the necessary packages
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+
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+ ```bash
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+ pip install --upgrade autoawq autoawq-kernels
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+ ```
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+
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+ ### Example Python code
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+
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+ ```python
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+ from awq import AutoAWQForCausalLM
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+ from transformers import AutoTokenizer, TextStreamer
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+
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+ model_path = "solidrust/Average_Normie_v3.69_8B-AWQ"
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+ system_message = "You are Average_Normie_v3.69_8B, incarnated as a powerful AI. You were created by jeiku."
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+
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+ # Load model
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+ model = AutoAWQForCausalLM.from_quantized(model_path,
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+ fuse_layers=True)
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+ tokenizer = AutoTokenizer.from_pretrained(model_path,
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+ trust_remote_code=True)
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+ streamer = TextStreamer(tokenizer,
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+ skip_prompt=True,
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+ skip_special_tokens=True)
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+
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+ # Convert prompt to tokens
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+ prompt_template = """\
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+ <|im_start|>system
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+ {system_message}<|im_end|>
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+ <|im_start|>user
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+ {prompt}<|im_end|>
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+ <|im_start|>assistant"""
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+
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+ prompt = "You're standing on the surface of the Earth. "\
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+ "You walk one mile south, one mile west and one mile north. "\
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+ "You end up exactly where you started. Where are you?"
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+
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+ tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt),
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+ return_tensors='pt').input_ids.cuda()
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+
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+ # Generate output
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+ generation_output = model.generate(tokens,
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+ streamer=streamer,
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+ max_new_tokens=512)
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+ ```
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+
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  ### About AWQ
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  AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.