kunjcr2
/

llama3-3b-lora-openhermes

@@ -1,199 +1,118 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+tags:
+- Assistant
+license: apache-2.0
+datasets:
+- teknium/OpenHermes-2.5
+language:
+- en
+base_model:
+- meta-llama/Llama-3.2-3B
 ---
+# Llama-3.2-3B LoRA Fine-tune on OpenHermes
+## 📖 Overview
+This model is a **LoRA fine-tuned version of `meta-llama/Llama-3.2-3B`** on the [OpenHermes dataset](https://huggingface.co/datasets/teknium/OpenHermes-2.5).
+The goal of this run was to adapt Llama-3.2-3B for improved instruction-following using a high-quality, multi-domain SFT dataset.
+Fine-tuning was performed with **parameter-efficient fine-tuning (PEFT)** using **LoRA adapters**. Only \~0.75% of model parameters were trained, keeping compute and memory usage efficient while still yielding strong gains.
+---
+## ⚙️ Training Configuration
+**Base Model:** `meta-llama/Llama-3.2-3B`
+**Method:** QLoRA (LoRA rank 16, α=32, dropout=0.05)
+**Trainable Parameters:** 24.3M / 3.24B (\~0.75%)
+**Training Arguments:**
+```python
+training_args = TrainingArguments(
+    output_dir="./llama_finetune_lora",
+    per_device_train_batch_size=2,
+    gradient_accumulation_steps=8,
+    learning_rate=2e-4,
+    num_train_epochs=1,
+    lr_scheduler_type="cosine",
+    warmup_ratio=0.03,
+    weight_decay=0.01,
+    logging_steps=200,
+    evaluation_strategy="steps",
+    eval_steps=200,
+    save_strategy="steps",
+    save_steps=1000,
+    save_total_limit=2,
+    load_best_model_at_end=True,
+    metric_for_best_model="eval_loss",
+    greater_is_better=False,
+    bf16=True,  # A100 support
+    fp16=False,
+    gradient_checkpointing=True,
+    torch_compile=False,
+    report_to="none",
+    seed=42
+)
+```
+---
+## 📊 Training Metrics
+Run stopped at **2000 steps** (\~4.5h on A100). Loss steadily improved and validation stabilized around 0.20.
+| Step | Training Loss | Validation Loss |
+| ---- | ------------- | --------------- |
+| 200  | 1.2781        | 0.2202          |
+| 400  | 0.2167        | 0.2134          |
+| 600  | 0.2139        | 0.2098          |
+| 800  | 0.2120        | 0.2072          |
+| 1000 | 0.2085        | 0.2057          |
+| 1200 | 0.1996        | 0.2043          |
+| 1400 | 0.2056        | 0.2034          |
+| 1600 | 0.2016        | 0.2023          |
+| 1800 | 0.2000        | 0.2012          |
+| 2000 | 0.2027        | 0.2005          |
+📉 **Validation loss converged near \~0.20**, indicating effective adaptation.
+---
+## 🚀 Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+base = "meta-llama/Llama-3.2-3B"
+adapter = "kunjcr2/llama3-3b-lora-openhermes"  # replace with your Hub repo
+# Load base + adapter
+tok = AutoTokenizer.from_pretrained(adapter)
+model = AutoModelForCausalLM.from_pretrained(base, torch_dtype="auto", device_map="auto")
+model = PeftModel.from_pretrained(model, adapter)
+# Generate
+prompt = "Explain the concept of binary search trees."
+inputs = tok(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=256)
+print(tok.decode(outputs[0], skip_special_tokens=True))
+```
+---
+## 📌 Notes
+* Training was run with **bf16 + gradient checkpointing** on A100 (40GB).
+* Only adapters are uploaded (small size). Use together with the base model.
+* Repo includes: `adapter_model.safetensors`, `adapter_config.json`, tokenizer files, and this README.
+* Training stopped early at **2000 steps (\~17% of planned)** due to good convergence.
+---
+✨ If you like this model, feel free to try it out and extend training. Future runs could include more steps, preference tuning (DPO/ORPO), or domain-specific mixtures.