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Model: devshaheen/Llama-2-7b-chat-finetune
Source: Original Platform

README.md

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+---
+license: mit
+datasets:
+  - mlabonne/guanaco-llama2-1k
+language:
+  - en
+base_model:
+  - NousResearch/Llama-2-7b-chat-hf
+pipeline_tag: text-generation
+library_name: transformers
+finetuned_model: true
+model_type: causal-lm
+finetuned_task: instruction-following
+tags:
+  - instruction-following
+  - text-generation
+  - fine-tuned
+  - llama2
+  - causal-language-model
+  - QLoRa
+  - 4-bit-quantization
+  - low-memory
+  - training-optimized
+metrics:
+  - accuracy
+  - loss
+---
+
+# Llama-2-7B-Chat Fine-Tuned Model
+
+This model is a fine-tuned version of **Llama-2-7B-Chat** model, optimized for instruction-following tasks. It has been trained on the `mlabonne/guanaco-llama2-1k` dataset and is optimized for efficient text generation across various NLP tasks, including question answering, summarization, and text completion.
+
+## Model Details
+- **Base Model**: NousResearch/Llama-2-7b-chat-hf
+- **Fine-Tuning Task**: Instruction-following
+- **Training Dataset**: mlabonne/guanaco-llama2-1k
+- **Optimized For**: Text generation, question answering, summarization, and more.
+- **Fine-Tuned Parameters**: 
+  - **LoRA** (Low-Rank Adaption) applied for efficient training with smaller parameter updates.
+  - Quantized to **4-bit** for memory efficiency and better GPU utilization.
+  - Training includes **gradient accumulation**, **gradient checkpointing**, and **weight decay** to prevent overfitting and enhance memory efficiency.
+
+## Usage
+
+You can use this fine-tuned model with the Hugging Face `transformers` library. Below is an example of how to load and use the model for text generation.
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load pre-trained model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("https://huggingface.co/devshaheen/llama-2-7b-chat-finetune")
+model = AutoModelForCausalLM.from_pretrained("https://huggingface.co/devshaheen/llama-2-7b-chat-finetune")
+
+# Example text generation
+input_text = "What is the capital of France?"
+inputs = tokenizer(input_text, return_tensors="pt")
+outputs = model.generate(**inputs)
+generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+print(generated_text)
+
+
+
+
+
+