ylliprifti
/

hackathon-2025

@@ -1,207 +1,88 @@
 ---
-base_model: codellama/CodeLlama-7b-Instruct-hf
-library_name: peft
-pipeline_tag: text-generation
 tags:
-- base_model:adapter:codellama/CodeLlama-7b-Instruct-hf
-- lora
-- transformers
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
-### Model Description
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-### Framework versions
-- PEFT 0.18.0

 ---
+license: mit
+base_model: meta-llama/Meta-Llama-3-8B
 tags:
+  - llama
+  - fine-tuned
+  - lora
+  - peft
+language:
+  - en
+pipeline_tag: text-generation
 ---
+# Fine-Tuned LLM Model
+This model is a fine-tuned version of [Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) using LoRA (Low-Rank Adaptation).
 ## Model Details
+- **Base Model:** meta-llama/Meta-Llama-3-8B
+- **Fine-tuning Method:** LoRA (PEFT)
+- **Language:** English
+### Training Configuration
+| Parameter | Value |
+|-----------|-------|
+| LoRA Rank (r) | 64 |
+| LoRA Alpha | 128 |
+| Target Modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
+| Learning Rate | 0.0025 |
+| LR Scheduler | Cosine |
+| Warmup Ratio | 0.03 |
+| Epochs | 16 |
+| Batch Size | 8 |
+| Max Length | 512 |
+| Precision | BF16 |
+## Usage
+### With Transformers + PEFT
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+# Load base model
+base_model = AutoModelForCausalLM.from_pretrained(
+    "meta-llama/Meta-Llama-3-8B",
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+# Load fine-tuned adapter
+model = PeftModel.from_pretrained(base_model, "ylliprifti/hackathon-2025")
+tokenizer = AutoTokenizer.from_pretrained("ylliprifti/hackathon-2025")
+# Generate
+inputs = tokenizer("Your prompt here", return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=256)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+### Merge Adapter (Optional)
+```python
+# Merge LoRA weights into base model for faster inference
+merged_model = model.merge_and_unload()
+merged_model.save_pretrained("merged-model")
+```
+## Training Data
+This model was fine-tuned on domain-specific conversational data.
+## Limitations
+- This model inherits the limitations of the base Llama 3 model
+- Performance may vary on tasks outside the fine-tuning domain
+- Not suitable for production use without further evaluation
+## License
+This model is released under the MIT License. The base model (Llama 3) is subject to Meta's license terms.
+---
+*Fine-tuned using [LLM Fine-Tuning MLOps Pipeline](https://github.com/)*