Tanneru
/

CodeLlama-7b-Python-hf-ft

@@ -1,199 +1,150 @@
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+---
+license: apache-2.0
+datasets:
+- jtatman/python-code-dataset-500k
+metrics:
+- bleu
+- rouge
+- perplexity
+- chrf
+- codebertscore
+base_model:
+- codellama/CodeLlama-7b-Python-hf
+pipeline_tag: text-generation
+tags:
+- code
+- python
+- codellama
+- lora
+- peft
+- sft
+- programming
+---
+# CodeLlama-7b-Python-hf-ft
+This repository contains a **LoRA fine-tuned adapter** for **[CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf)**, trained to improve **Python instruction-following and code generation**.
+**Note:**
+This is a **PEFT LoRA adapter**, not a fully merged standalone model. You must load it on top of the base model.
+---
+## Model Details
+- **Base model**: [codellama/CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf)
+- **Fine-tuned for**: Python instruction-following and code generation
+- **Fine-tuning method**: SFT + LoRA (PEFT)
+- **Framework**: Transformers + PEFT + TRL
+---
+## Dataset Used
+This adapter was fine-tuned on:
+1. [jtatman/python-code-dataset-500k](https://huggingface.co/datasets/jtatman/python-code-dataset-500k)
+   - Large-scale Python instruction → solution pairs
+   - Parquet format (~500k+ examples)
+---
+## Training Configuration
+### LoRA Configuration
+- **r:** 32
+- **lora_alpha:** 16
+- **Target modules:**
+  `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
+### SFT Configuration
+- **Epochs:** 1
+- **Learning rate:** 2e-4
+- **Scheduler:** cosine
+- **Warmup ratio:** 0.03
+- **Weight decay:** 0.0
+- **Train batch size:** 4
+- **Eval batch size:** 4
+- **Gradient accumulation steps:** 16
+- **Precision:** bf16
+- **Attention:** flash_attention_2
+- **Packing:** enabled
+- **Gradient checkpointing:** enabled
+- **Logging:** every 50 steps + per epoch
+- **Saving:** per epoch (`save_total_limit=2`)
+---
+## Evaluation Results
+The model was evaluated using both language-modeling metrics and generation-quality metrics.
+### 📉 Perplexity / Loss
+- **Base model loss:** `1.3214`
+- **Base model perplexity:** `3.7486`
+- **Fine-tuned (LoRA) val/test loss:** `0.7126`
+- **Fine-tuned (LoRA) val/test perplexity:** `2.0394`
+### 📊 Generation Quality Metrics (Test)
+- **Exact Match:** `0.0033`
+- **Normalized Exact Match:** `0.0033`
+- **BLEU:** `18.43`
+- **chrF:** `34.06`
+- **ROUGE-L (F1):** `0.2417`
+### 🧠 CodeBERTScore (Mean)
+- **Precision:** `0.7187`
+- **Recall:** `0.7724`
+- **F1:** `0.7421`
+- **F3:** `0.7657`
+### 🧾 Training Summary (from logs)
+- **Train loss:** `~0.6903`
+- **Eval loss:** `~0.6877`
+---
+## Example Usage
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+# Base + adapter
+base_id = "codellama/CodeLlama-7b-Python-hf"
+adapter_id = "Tanneru/CodeLlama-7b-Python-hf-ft"
+# Load tokenizer (repo includes tokenizer files)
+tokenizer = AutoTokenizer.from_pretrained(adapter_id)
+# Load base model
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_id,
+    device_map="auto",
+    torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
+)
+# Load LoRA adapter
+model = PeftModel.from_pretrained(base_model, adapter_id)
+model.eval()
+prompt = "Write a Python function that checks if a number is prime."
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+with torch.inference_mode():
+    out = model.generate(**inputs, max_new_tokens=256)
+print(tokenizer.decode(out[0], skip_special_tokens=True))
+@misc{tanneru2025codellamapythonft,
+  title   = {CodeLlama-7b-Python-hf-ft},
+  author  = {Tanneru},
+  year    = {2025},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/Tanneru/CodeLlama-7b-Python-hf-ft}}
+}