pranjalsingh
/

alpaca-Llama-3.1-70B-Instruct-chat

@@ -1,202 +1,118 @@
----
-base_model: meta-llama/Llama-3.3-70B-Instruct
-library_name: peft
----
-# Model Card for Model ID
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-### Framework versions
-- PEFT 0.12.0

+# 🧠 Model Card: `pranjalsingh/alpaca-Llama-3.1-70B-Instruct-chat`
+A LoRA fine-tuned version of the **meta-llama/Llama-3.1-70B-Instruct** model on the **Alpaca dataset**, optimized using **PEFT** and accelerated on **Intel Gaudi3 HPU** hardware.
+---
+## 📝 Model Summary
+This model is a fine-tuned variant of LLaMA 3.1 70B Instruct, trained on the Alpaca dataset using Parameter-Efficient Fine-Tuning (PEFT) via LoRA. The goal of this fine-tuning was to improve instruction-following performance on lightweight resources, leveraging Intel’s Gaudi3 HPU for efficient training.
+---
+## 📄 Model Details
+* **Base Model:** `meta-llama/Llama-3.1-70B-Instruct`
+* **Fine-tuned Model:** `pranjalsingh/alpaca-Llama-3.1-70B-Instruct-chat`
+* **Fine-tuned By:** *Pranjal Singh Thakur*
+* **Dataset:** Stanford Alpaca dataset
+* **PEFT Library:** PEFT v0.12.0
+* **Fine-tuning Technique:** LoRA
+* **Epochs:** 2
+* **Training Hardware:** 1 Node with 8× Intel Gaudi3 HPUs
+* **Language(s):** English
+* **License:** Same as base model (LLaMA 3)
+* **Credit:** Intel for providing Gaudi3 HPU infrastructure
+---
+## 🚀 Usage
 ### Direct Use
+Use the model as an instruction-following chatbot or in downstream applications requiring LLM completion with lightweight deployment using LoRA adapters.
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-70B-Instruct")
+tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-70B-Instruct")
+model = PeftModel.from_pretrained(base_model, "pranjalsingh/alpaca-Llama-3.1-70B-Instruct-chat")
+inputs = tokenizer("### Instruction: Explain quantum computing in simple terms.", return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=256)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+---
+## 📊 Evaluation Results
+| Metric                 | Value     |
+| ---------------------- | --------- |
+| Eval Accuracy          | 73.27%    |
+| Eval Loss              | 1.02      |
+| Perplexity             | 2.79      |
+| Evaluation Runtime     | 20.97s    |
+| Samples Evaluated      | 101       |
+| Samples/Sec            | 4.82      |
+| Max Memory Used (GB)   | 126.2     |
+| Total Available Memory | 126.54 GB |
+| Memory Allocated (GB)  | 41.06     |
+---
+## 🛠 Training Configuration
+* **Epochs:** 2
+* **Precision:** Likely mixed precision (bf16/fp16 on Gaudi3)
+* **Hardware:** Intel Gaudi3 HPU (8 cards, 1 node)
+* **Frameworks:** PEFT, Hugging Face Transformers
+* **Batching & Tokenization:** Not explicitly provided
+---
+## 📦 Model Sources
+* **Repository:** [Hugging Face Model Card](https://huggingface.co/pranjalsingh/alpaca-Llama-3.1-70B-Instruct-chat)
+* **Dataset:** [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca)
+* **Base Model:** [`meta-llama/Llama-3.1-70B-Instruct`](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct)
+---
+## ⚠️ Limitations & Risks
+* Not suitable for multilingual tasks (trained only on English data).
+* May reflect biases present in the Alpaca dataset.
+* Not recommended for sensitive or safety-critical applications.
+* Fine-tuning was conducted for instruction tasks — may not generalize to other domains.
+---
+## ♻️ Environmental Impact
+| Parameter         | Value                                                       |
+| ----------------- | ----------------------------------------------------------- |
+| Compute Platform  | Intel Gaudi3                                                |
+| Cards Used        | 8× HPU                                                      |
+| Training Duration | \~2 Epochs                                                  |
+| Region            | \[More info needed]                                         |
+| Emission Estimate | \[Use [MLCO2](https://mlco2.github.io/impact) to calculate] |
+---
+## 👨‍💻 Author & Acknowledgment
+* **Author:** Pranjal Singh Thakur
+* **Credit:** Intel (for compute resources using Gaudi3 HPU)
+---
+## 🔖 Citation
+Coming soon.