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Duplicate from bartowski/wavecoder-ultra-1.1-6.7b-AWQ

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Co-authored-by: Bartowski <bartowski@users.noreply.huggingface.co>

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README.md ADDED
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+ ---
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+ license: mit
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+ license_link: https://huggingface.co/microsoft/wavecoder-ultra-6.7b/blob/main/LICENSE
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+ language:
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+ - en
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+ library_name: transformers
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+ datasets:
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+ - humaneval
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+ pipeline_tag: text-generation
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+ tags:
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+ - code
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+ metrics:
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+ - code_eval
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+ quantized_by: bartowski
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+ ---
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+
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+ ## 4-bit GEMM AWQ Quantizations of wavecoder-ultra-1.1-6.7b
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+
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+ Using <a href="https://github.com/casper-hansen/AutoAWQ/">AutoAWQ</a> release <a href="https://github.com/casper-hansen/AutoAWQ/releases/tag/v0.2.4">v0.2.4</a> for quantization.
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+
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+ Original model: https://huggingface.co/microsoft/wavecoder-ultra-6.7b
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+
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+ Note: They updated the model in place, no idea if there's actually any changes, but tagging this 1.1 just in case
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+
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+ ## Prompt format
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+
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+ No chat template specified so default is used. This may be incorrect, check original model card for details.
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+
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+ ```
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+ <|begin▁of▁sentence|>[INST] <<SYS>>
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+ {system_prompt}
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+ <</SYS>>
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+
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+ {prompt} [/INST] <|end▁of▁sentence|>
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+ ```
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+
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+ ## AWQ Parameters
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+
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+ - q_group_size: 128
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+ - w_bit: 4
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+ - zero_point: True
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+ - version: GEMM
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+
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+ ## How to run
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+
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+ From the AutoAWQ repo [here](https://github.com/casper-hansen/AutoAWQ/blob/main/examples/generate.py)
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+
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+ First install autoawq pypi package:
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+
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+ ```
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+ pip install autoawq
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+ ```
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+
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+ Then run the following:
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+
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+ ```
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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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+
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+ quant_path = "models/wavecoder-ultra-1.1-6.7b-AWQ"
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+
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+ # Load model
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+ model = AutoAWQForCausalLM.from_quantized(quant_path, fuse_layers=True)
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+ tokenizer = AutoTokenizer.from_pretrained(quant_path, trust_remote_code=True)
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+ streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
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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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+ chat = [
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+ {"role": "system", "content": "You are a concise assistant that helps answer questions."},
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+ {"role": "user", "content": prompt},
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+ ]
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+
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+ # <|eot_id|> used for llama 3 models
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+ terminators = [
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+ tokenizer.eos_token_id,
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+ tokenizer.convert_tokens_to_ids("<|eot_id|>")
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+ ]
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+
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+ tokens = tokenizer.apply_chat_template(
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+ chat,
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+ return_tensors="pt"
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+ ).cuda()
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+
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+ # Generate output
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+ generation_output = model.generate(
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+ tokens,
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+ streamer=streamer,
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+ max_new_tokens=64,
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+ eos_token_id=terminators
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+ )
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+ ```
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+
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+ Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
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