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Duplicate from tloen/alpaca-lora

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Co-authored-by: Eric J. Wang <tloen@users.noreply.huggingface.co>

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  1. .gitattributes +34 -0
  2. README.md +12 -0
  3. app.py +165 -0
  4. requirements.txt +8 -0
.gitattributes ADDED
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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.lfs.* filter=lfs diff=lfs merge=lfs -text
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+ *.mlmodel filter=lfs diff=lfs merge=lfs -text
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+ *.model filter=lfs diff=lfs merge=lfs -text
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+ *.pickle filter=lfs diff=lfs merge=lfs -text
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+ *.pkl filter=lfs diff=lfs merge=lfs -text
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+ *.pt filter=lfs diff=lfs merge=lfs -text
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+ *.safetensors filter=lfs diff=lfs merge=lfs -text
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+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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+ ---
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+ title: Alpaca-LoRA
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+ emoji: 🦙
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+ colorFrom: red
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+ colorTo: gray
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+ sdk: gradio
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+ sdk_version: 3.21.0
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+ app_file: app.py
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+ pinned: false
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+ license: apache-2.0
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+ duplicated_from: tloen/alpaca-lora
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+ ---
app.py ADDED
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+ import torch
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+ from peft import PeftModel
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+ import transformers
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+ import gradio as gr
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+
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+ assert (
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+ "LlamaTokenizer" in transformers._import_structure["models.llama"]
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+ ), "LLaMA is now in HuggingFace's main branch.\nPlease reinstall it: pip uninstall transformers && pip install git+https://github.com/huggingface/transformers.git"
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+ from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig
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+
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+ tokenizer = LlamaTokenizer.from_pretrained("decapoda-research/llama-7b-hf")
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+
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+ BASE_MODEL = "decapoda-research/llama-7b-hf"
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+ LORA_WEIGHTS = "tloen/alpaca-lora-7b"
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+
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+ if torch.cuda.is_available():
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+ device = "cuda"
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+ else:
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+ device = "cpu"
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+
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+ try:
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+ if torch.backends.mps.is_available():
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+ device = "mps"
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+ except:
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+ pass
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+
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+ if device == "cuda":
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+ model = LlamaForCausalLM.from_pretrained(
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+ BASE_MODEL,
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+ load_in_8bit=False,
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+ torch_dtype=torch.float16,
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+ device_map="auto",
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+ )
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+ model = PeftModel.from_pretrained(
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+ model, LORA_WEIGHTS, torch_dtype=torch.float16, force_download=True
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+ )
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+ elif device == "mps":
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+ model = LlamaForCausalLM.from_pretrained(
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+ BASE_MODEL,
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+ device_map={"": device},
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+ torch_dtype=torch.float16,
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+ )
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+ model = PeftModel.from_pretrained(
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+ model,
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+ LORA_WEIGHTS,
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+ device_map={"": device},
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+ torch_dtype=torch.float16,
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+ )
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+ else:
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+ model = LlamaForCausalLM.from_pretrained(
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+ BASE_MODEL, device_map={"": device}, low_cpu_mem_usage=True
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+ )
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+ model = PeftModel.from_pretrained(
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+ model,
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+ LORA_WEIGHTS,
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+ device_map={"": device},
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+ )
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+
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+
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+ def generate_prompt(instruction, input=None):
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+ if input:
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+ return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
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+
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+ ### Instruction:
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+ {instruction}
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+
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+ ### Input:
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+ {input}
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+
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+ ### Response:"""
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+ else:
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+ return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
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+
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+ ### Instruction:
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+ {instruction}
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+
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+ ### Response:"""
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+
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+ if device != "cpu":
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+ model.half()
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+ model.eval()
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+ if torch.__version__ >= "2":
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+ model = torch.compile(model)
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+
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+
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+ def evaluate(
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+ instruction,
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+ input=None,
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+ temperature=0.1,
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+ top_p=0.75,
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+ top_k=40,
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+ num_beams=4,
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+ max_new_tokens=128,
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+ **kwargs,
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+ ):
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+ prompt = generate_prompt(instruction, input)
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+ inputs = tokenizer(prompt, return_tensors="pt")
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+ input_ids = inputs["input_ids"].to(device)
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+ generation_config = GenerationConfig(
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+ temperature=temperature,
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+ top_p=top_p,
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+ top_k=top_k,
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+ num_beams=num_beams,
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+ **kwargs,
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+ )
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+ with torch.no_grad():
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+ generation_output = model.generate(
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+ input_ids=input_ids,
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+ generation_config=generation_config,
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+ return_dict_in_generate=True,
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+ output_scores=True,
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+ max_new_tokens=max_new_tokens,
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+ )
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+ s = generation_output.sequences[0]
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+ output = tokenizer.decode(s)
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+ return output.split("### Response:")[1].strip()
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+
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+
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+ g = gr.Interface(
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+ fn=evaluate,
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+ inputs=[
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+ gr.components.Textbox(
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+ lines=2, label="Instruction", placeholder="Tell me about alpacas."
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+ ),
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+ gr.components.Textbox(lines=2, label="Input", placeholder="none"),
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+ gr.components.Slider(minimum=0, maximum=1, value=0.1, label="Temperature"),
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+ gr.components.Slider(minimum=0, maximum=1, value=0.75, label="Top p"),
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+ gr.components.Slider(minimum=0, maximum=100, step=1, value=40, label="Top k"),
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+ gr.components.Slider(minimum=1, maximum=4, step=1, value=4, label="Beams"),
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+ gr.components.Slider(
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+ minimum=1, maximum=512, step=1, value=128, label="Max tokens"
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+ ),
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+ ],
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+ outputs=[
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+ gr.inputs.Textbox(
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+ lines=5,
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+ label="Output",
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+ )
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+ ],
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+ title="🦙🌲 Alpaca-LoRA",
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+ description="Alpaca-LoRA is a 7B-parameter LLaMA model finetuned to follow instructions. It is trained on the [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) dataset and makes use of the Huggingface LLaMA implementation. For more information, please visit [the project's website](https://github.com/tloen/alpaca-lora).",
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+ )
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+ g.queue(concurrency_count=1)
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+ g.launch()
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+
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+ # Old testing code follows.
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+
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+ """
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+ if __name__ == "__main__":
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+ # testing code for readme
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+ for instruction in [
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+ "Tell me about alpacas.",
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+ "Tell me about the president of Mexico in 2019.",
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+ "Tell me about the king of France in 2019.",
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+ "List all Canadian provinces in alphabetical order.",
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+ "Write a Python program that prints the first 10 Fibonacci numbers.",
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+ "Write a program that prints the numbers from 1 to 100. But for multiples of three print 'Fizz' instead of the number and for the multiples of five print 'Buzz'. For numbers which are multiples of both three and five print 'FizzBuzz'.",
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+ "Tell me five words that rhyme with 'shock'.",
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+ "Translate the sentence 'I have no mouth but I must scream' into Spanish.",
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+ "Count up from 1 to 500.",
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+ ]:
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+ print("Instruction:", instruction)
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+ print("Response:", evaluate(instruction))
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+ print()
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+ """
requirements.txt ADDED
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+ datasets
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+ loralib
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+ sentencepiece
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+ git+https://github.com/huggingface/transformers.git
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+ accelerate
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+ bitsandbytes
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+ git+https://github.com/huggingface/peft.git
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+ gradio