Update README.md
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README.md
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@@ -13,6 +13,8 @@ import logging
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig
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# Configure logging to see warnings and debug information
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logging.basicConfig(
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@@ -47,41 +49,41 @@ tokenizer = AutoTokenizer.from_pretrained(model_id)
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# Push to hub
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MODEL_NAME = model_id.split("/")[-1]
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save_to = f"torchao-testing/{MODEL_NAME}-IntxWeightOnlyConfig-v{version}-0.14.0.dev
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quantized_model.push_to_hub(save_to, safe_serialization=False)
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tokenizer.push_to_hub(save_to)
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# Manual Testing
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prompt = "
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print("Prompt:", prompt)
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inputs = tokenizer(
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prompt,
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return_tensors="pt",
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).to("cuda")
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#
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generated_ids = quantized_model.generate(**inputs, max_new_tokens=128, temperature=0)
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)
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print("Response:", correct_output_text[0][len(prompt) :])
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)
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generated_ids = reloaded_model.generate(**inputs, max_new_tokens=128, temperature=0)
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output_text = tokenizer.batch_decode(
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generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
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)
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print("Response:", output_text[0][len(prompt) :])
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assert(correct_output_text == output_text)
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```
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig
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from huggingface_hub import HfApi
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import io
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# Configure logging to see warnings and debug information
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logging.basicConfig(
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# Push to hub
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MODEL_NAME = model_id.split("/")[-1]
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save_to = f"torchao-testing/{MODEL_NAME}-IntxWeightOnlyConfig-v{version}-0.14.0.dev"
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quantized_model.push_to_hub(save_to, safe_serialization=False)
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tokenizer.push_to_hub(save_to)
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# Manual Testing
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prompt = "Hey, are you conscious? Can you talk to me?"
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print("Prompt:", prompt)
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inputs = tokenizer(
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prompt,
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return_tensors="pt",
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).to("cuda")
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# setting temperature to 0 to make sure result deterministic
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generated_ids = quantized_model.generate(**inputs, max_new_tokens=128, temperature=0)
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api = HfApi()
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buf = io.BytesIO()
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torch.save(prompt, buf)
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api.upload_file(
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path_or_fileobj=buf,
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path_in_repo="model_prompt.pt",
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repo_id=save_to,
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)
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buf = io.BytesIO()
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torch.save(generated_ids, buf)
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api.upload_file(
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path_or_fileobj=buf,
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path_in_repo="model_output.pt",
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repo_id=save_to,
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)
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output_text = tokenizer.batch_decode(
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generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
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)
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print("Response:", output_text[0][len(prompt) :])
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```
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