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+ ---
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+ license: mit
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+ tags:
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+ - llama
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+ - text-generation
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+ - instruction-following
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+ - llama-2
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+ - lora
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+ - peft
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+ - trl
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+ - sft
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+ ---
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+
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+ # Llama-2-7b-chat-finetune
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+
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+ This model is a fine-tuned version of [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf) using the [mlabonne/guanaco-llama2-1k](https://huggingface.co/datasets/mlabonne/guanaco-llama2-1k) dataset. It has been fine-tuned using LoRA (Low-Rank Adaptation) with the PEFT library and the SFTTrainer from TRL.
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+
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+ ## Model Description
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+
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+ This model is intended for text generation and instruction following tasks. It has been fine-tuned on a dataset of 1,000 instruction-following examples.
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+
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+ ## Intended Uses & Limitations
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+
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+ This model can be used for a variety of text generation tasks, including:
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+
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+ * Generating creative text formats, like poems, code, scripts, musical pieces, email, letters, etc.
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+ * Answering your questions in an informative way, even if they are open ended, challenging, or strange.
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+ * Following your instructions and completing your requests thoughtfully.
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+
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+
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+ Limitations:
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+
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+ * The model may generate biased or harmful content.
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+ * The model may not be able to follow all instructions perfectly.
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+ * The model may not be able to generate text that is factually accurate.
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+
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+ ## Training and Fine-tuning
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+
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+ This model was fine-tuned using the following parameters:
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+
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+ * LoRA attention dimension (lora_r): 64
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+ * Alpha parameter for LoRA scaling (lora_alpha): 16
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+ * Dropout probability for LoRA layers (lora_dropout): 0.1
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+ * 4-bit precision base model loading (use_4bit): True
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+ * Number of training epochs (num_train_epochs): 1
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+ * Batch size per GPU for training (per_device_train_batch_size): 4
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+ * Learning rate (learning_rate): 2e-4
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+
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+ ## How to Use
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+
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+ You can use this model with the following code:
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+ ```
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+ from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
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+
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+ model_name = "chaitanya42/Llama-2-7b-chat-finetune"
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+ tokenizer = AutoTokenizer.from_pretrained(model_name)
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+ model = AutoModelForCausalLM.from_pretrained(model_name)
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+
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+ prompt = "What is a large language model?"
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+ pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
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+ result = pipe(f"[INST] {prompt} [/INST]")
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+ print(result[0]['generated_text'])
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+ ```