Upload locally saved GRIT fine-tuned model: open_llama_3b_v2 on Alpaca

README.md

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+---
+tags:
+- llama3
+- alpaca
+- grit
+- lora
+- qlora
+- instruction-tuning
+- fine-tuned
+base_model: openlm-research/open_llama_3b_v2
+library_name: peft
+license: apache-2.0
+datasets:
+- tatsu-lab/alpaca
+language:
+- en
+pipeline_tag: text-generation
+---
+
+# OpenLlama-3B-v2 Fine-tuned with GRIT and QLoRA
+
+This model is a fine-tuned version of [openlm-research/open_llama_3b_v2](https://huggingface.co/openlm-research/open_llama_3b_v2) using the **GRIT** (Gradient Regularized Instruction Tuning) algorithm and **QLoRA** on the [Alpaca dataset](https://huggingface.co/datasets/tatsu-lab/alpaca).
+
+The base model is quantized to 4-bit (NF4) to enable efficient fine-tuning.
+
+## 🚀 Training Details
+
+### GRIT Algorithm
+- **K-FAC Updates**: Every 200 steps for second-order preconditioning
+- **Neural Reprojection**: Every 500 steps for rank optimization
+- **Optimized LoRA Modules**: attention + key MLP layers (as per design)
+
+### Fine-tuning Configuration
+- **Base Model**: OpenLlama 3B v2
+- **Quantization**: 4-bit (NF4) with float16 compute
+- **LoRA Rank**: 64  
+- **LoRA Alpha**: 128  
+- **Batch Size**: 16 (per device)  
+- **Gradient Accumulation**: 4 (Effective batch = 64)  
+- **Learning Rate**: 5.0e-05  
+- **Precision**: bf16 mixed precision  
+- **Sequence Length**: 512 tokens  
+- **Gradient Checkpointing**: Enabled
+
+### Performance Improvements
+- ✅ **Faster Convergence**: K-FAC preconditioning aligns updates with curvature
+- ✅ **Memory-Efficient**: 4-bit quantization (QLoRA) and gradient checkpointing used.
+- ✅ **Efficient Training**: Utilizes `accelerate` for efficient training.
+
+## 📊 Training Metrics (sample)
+- **Total Steps**: N/A (training session interrupted)
+- **Final Loss**: N/A (training session interrupted)
+- **BLEU (val)**: N/A (training session interrupted)
+- **Trainable Params**: N/A (training session interrupted)
+
+## 🏷️ Model Tags
+- Instruction-tuned with GRIT and QLoRA
+- GRIT-tuned Model
+- 4-bit Quantized Model
+- LoRA rank 64  
+- Mixed precision (bf16)  
+- Alpaca dataset fine-tuning  
+
+## 📝 Algorithm Details
+- **K-FAC Preconditioning** (Natural Gradient) and **Neural Reprojection** as per GRIT method
+- **Memory Efficient**: Covariance matrices on CPU to reduce GPU load
+
+## 🏆 Results
+In benchmark comparisons, GRIT has shown **faster convergence and better stability** than standard LoRA or fine-tuning, making it well-suited for efficient single-epoch training.
+
+## 📝 Citation
+If you use this model, please cite:
+```bibtex
+@misc{grit-openllama-3b-alpaca,
+  title={OpenLlama 3B v2 Fine-tuned with GRIT on Alpaca},
+  author={Pritish92},
+  year={2025},
+  publisher={Hugging Face},
+  url={https://huggingface.co/Pritish92/open-llama-3b-v2-grit-alpaca}
+}
+```
+
+## ⚖️ License
+This model inherits the Apache 2.0 license.

adapter_config.json

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+  "base_model_name_or_path": "openlm-research/open_llama_3b_v2",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
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+  "inference_mode": true,
+  "init_lora_weights": true,
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+  "layers_pattern": null,
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+  "loftq_config": {},
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+  "megatron_core": "megatron.core",
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+  "peft_type": "LORA",
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+  "rank_pattern": {},
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+  "target_modules": [
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+    "v_proj",
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+    "q_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

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special_tokens_map.json

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tokenizer.model

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tokenizer_config.json

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+}