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Create README.md
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README.md
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---
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datasets:
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- tbboukhari/Alpaca_french_instruct
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language:
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- fr
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library_name: transformers
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tags:
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- Alpaca
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- Instruction-fine-tuning
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- NLP
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- Instruct Alpaca
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- PEFT
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- LoRA
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---
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## How to use🦙:
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```py
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import torch
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import bitsandbytes as bnb
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from peft import PeftModel, PeftConfig, prepare_model_for_int8_training, LoraConfig, get_peft_model
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from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig
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peft_model_id = "tbboukhari/Alpaca_instruction_fine_tune_French"
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config = PeftConfig.from_pretrained(peft_model_id)
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tokenizer = LlamaTokenizer.from_pretrained(config.base_model_name_or_path)
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model = LlamaForCausalLM.from_pretrained(config.base_model_name_or_path,
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load_in_8bit=True,
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device_map="auto",)
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# Load the Lora model
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model = PeftModel.from_pretrained(model, peft_model_id)
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# Based on the inference code by `tloen/alpaca-lora`
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def generate_prompt(instruction, entree=None):
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if entree :
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return f"""Vous trouverez ci-dessous des instructions décrivant une tâche, ainsi qu'une entrée qui fournit plus de contexte. Rédigez une réponse qui complète convenablement la demande.
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### instructions:
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{instruction}
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### entrée:
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{entree}
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### sortie:"""
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else:
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return f"""Vous trouverez ci-dessous des instructions décrivant une tâche, ainsi qu'une entrée qui fournit plus de contexte. Rédigez une réponse qui complète convenablement la demande.
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### instructions:
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{instruction}
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### sortie:"""
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# Inputs to instantiate the model:
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generation_config = GenerationConfig(
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temperature=0.2,
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top_p=0.75,
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num_beams=4,
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)
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# Evaluate the model:
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def evaluate(instruction, input=None):
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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"].cuda()
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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=256
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)
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for s in generation_output.sequences:
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output = tokenizer.decode(s)
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print("sortie:", output.split("### sortie:")[1].strip())
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evaluate(input("instructions: "))
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```
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