Create README.md

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README.md

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