ejbejaranos
/

Bitnet-Llama3-from8BM-now2B

@@ -2,15 +2,11 @@
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 library_name: transformers
 tags: []
 ---
+# 🚀 BitNet-Llama3 (from 8B to 2B) Transformation & Training
+This project transforms a Llama3 model from 8B parameters to a BitNet architecture with 2B parameters, applying BitLinear layers. Additionally, the model is trained with a predefined dataset and uploaded to Hugging Face for future use.
+---
 ### Model Description
 <!-- Provide a longer summary of what this model is. -->
 <!-- Provide the basic links for the model. -->
+- **Repository:** ejbejaranos/Bitnet-Llama3-from8BM-now2B
+## 📄 Description
+This repository includes scripts to:
+1. 🎯 Transform a Llama3 model to a BitNet architecture.
+2. 💻 Train the model using Hugging Face and Weights & Biases.
+3. 🚀 Upload the transformed and trained model to Hugging Face for inference and future use.
+---
+## ⚙️ Requirements
+- Python 3.8+
+- Pytorch 1.10+
+- Transformers 4.0+
+- Hugging Face Hub API
+- Weights & Biases
+---
+## 🧰 Installation
+Make sure you have all required dependencies installed:
+```bash
+pip install torch transformers datasets wandb huggingface_hub
+```
+## 💥 How to Use
+1. Using the trained model for inference
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from utils.bitnet_transformation import replace_linears_in_hf
+# Load the BitNet model
+model = "ejbejaranos/Bitnet-Llama3-from8BM-now2B"
+model = AutoModelForCausalLM.from_pretrained(
+    model,
+    use_auth_token="YOUR_HF_TOKEN"
+)
+# Replace BitNet layers for inference
+replace_linears_in_hf(model)
+tokenizer = AutoTokenizer.from_pretrained("ejbejaranos/Bitnet-Llama3-from8BM-now2B")
+# Set up for inference
+model.to(device="cuda:0")
+prompt = "What is Machine Learning?"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+generate_ids = model.generate(inputs.input_ids, max_length=50)
+output = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+print(output)
+```
+---
+## 🧑‍🔬 Metrics
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6419c2f6b4adb0e101b17b6c/nCE1-KLDWDqSCmPtDMmWa.png)
+During training, the following metrics will be logged to Weights & Biases:
+- `final_loss`: 1.4.
+- `final_perplexity`: 4.2.
+---
+## 🎯 Future Goals
+- Implement additional quantization layers for inference.
+- Test the model on different datasets and contexts.
+---
+## 📢 Contact
+If you have questions, suggestions, or improvements, feel free to open an Issue or contact us through [Hugging Face](https://huggingface.co/ejbejaranos).
+---
 ## Environmental Impact
 - **Cloud Provider:** [More Information Needed]
 - **Compute Region:** [More Information Needed]
 - **Carbon Emitted:** [More Information Needed]
+-
+## 💡 Acknowledgments
+Thanks to [Hugging Face](https://huggingface.co/) and [Weights & Biases](https://wandb.ai/) for providing support and tools.