Re-upload model with updated configuration

README

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+---
+library_name: transformers
+tags:
+- causal-lm
+- llama
+- fine-tuned
+- text-generation
+---
+
+# Fine-Tuned LLaMA 3.2 1B Model
+
+This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on custom data. It has been trained to generate coherent and contextually relevant responses based on the input prompt.
+
+## Model Description
+
+- **Model Type**: LLaMA (Large Language Model for AI Assistants)
+- **Architecture**: Causal Language Model (LlamaForCausalLM)
+- **Base Model**: `meta-llama/Llama-3.2-1B-Instruct`
+- **Fine-Tuning**: Fine-tuned on domain-specific data to enhance performance on targeted tasks.
+- **Intended Use**: Suitable for various NLP tasks such as text generation, question answering, and code analysis.
+
+## Training Data
+
+The model was fine-tuned on a dataset containing domain-specific examples designed to improve its understanding and generation capabilities within specific contexts. The training data included:
+
+- **Code Samples**: Various programming languages for code analysis and explanation.
+- **Technical Documentation**: To improve technical writing and explanation capabilities.
+
+## Training Details
+
+- **Fine-Tuning Epochs**: 5
+- **Batch Size**: 1 (with gradient accumulation)
+- **Learning Rate**: 1e-5
+- **Hardware**: Fine-tuned using an NVIDIA A10G on an `g5.16xlarge` instance.
+- **Optimizer**: AdamW with weight decay
+
+### Model Configuration
+
+- **Hidden Size**: 2048
+- **Number of Layers**: 16
+- **Number of Attention Heads**: 32
+- **Intermediate Size**: 8192
+
+## Usage
+
+To use this model, you can either download it and run locally using the `transformers` library or use the Hugging Face Inference API.
+
+### Using with `transformers`
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load the fine-tuned model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("username/your-fine-tuned-llama")
+model = AutoModelForCausalLM.from_pretrained("username/your-fine-tuned-llama")
+
+# Generate text
+prompt = "What does EigenLayer do exactly?"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=150, num_beams=4, temperature=0.5, do_sample=True)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+### Using with the Hugging Face Inference API
+
+You can also use the model via the Hugging Face API endpoint:
+
+```python
+import requests
+
+API_URL = "https://api-inference.huggingface.co/models/username/your-fine-tuned-llama"
+headers = {"Authorization": "Bearer YOUR_HUGGING_FACE_API_TOKEN"}
+
+def query(prompt):
+    response = requests.post(API_URL, headers=headers, json={"inputs": prompt})
+    return response.json()
+
+print(query("Explain how EigenLayer functions."))
+```
+
+## Limitations
+
+- The model may generate incorrect or biased information. Users should verify the outputs for critical applications.
+- Due to fine-tuning, there might be domain-specific biases in the generation.
+
+## Ethical Considerations
+
+Please ensure that the outputs of this model are used responsibly. The model may generate unintended or harmful content, so it should be used with caution in sensitive applications.
+
+## Acknowledgements
+
+This model was fine-tuned based on [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct). Special thanks to the open-source community and contributors to the `transformers` library.