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README.md

@@ -72,7 +72,13 @@ The adapter was trained on a curated blend of English datasets:
 
 ## 🖥️ Training Hardware
 Fine-tuning was performed entirely on a consumer-grade laptop:
-- **Laptop:** Acer Nitro V15  - **GPU:** NVIDIA RTX 2050 Mobile (4 GB VRAM)  - **CPU:** Intel Core i5-13420H  - **RAM:** 16 GB  - **Quantization:** 4-bit NF4  - **Strategy:** Low VRAM setup using gradient accumulation, packing, and LoRA adapters  
+- **Laptop:** Acer Nitro V15
+- **GPU:** NVIDIA RTX 2050 Mobile (4 GB VRAM)
+- **CPU:** Intel Core i5-13420H
+- **RAM:** 16 GB
+- **Quantization:** 4-bit NF4
+- **Strategy:** Low VRAM setup using gradient accumulation, packing, and LoRA adapters
+-  
 This demonstrates that Phi-2 can be fine-tuned effectively even on low-VRAM devices.
 
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@@ -110,8 +116,9 @@ The adapter released in this repository is the result of this final, optimized t
 |--------|----------|----------|
 | **1** | Fine-tune again from scratch(from base model) by applying all the insights from previous experiments. | 1d 21h |
 
-📊 **W&B Log (Phase 1F):** [wandb.ai/VoidNova/.../runs/bpju3d09](https://wandb.ai/VoidNova/phi-2-2.7B_qlora_alpaca-51.8k_identity-model-232_squadv2-15k/runs/bpju3d09?nw=nwuseradhafajp)
-📊 **W&B Log (Final):** [wandb.ai/VoidNova/.../runs/rx5fih5v](https://wandb.ai/VoidNova/phi-2_qlora_ZeroChat/runs/rx5fih5v?nw=nwuseradhafajp)
+📊 **W&B Log (Phase 1F):** [wandb.ai/VoidNova/.../](https://wandb.ai/VoidNova/phi-2-2.7B_qlora_alpaca-51.8k_identity-model-232_squadv2-15k/)
+
+📊 **W&B Log (Final):** [wandb.ai/VoidNova/.../runs/rx5fih5v](https://wandb.ai/VoidNova/phi-2_qlora_ZeroChat/)
 
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