README.md

---
datasets:
- greengerong/leetcode
language:
- en
base_model:
- codellama/CodeLlama-7b-Instruct-hf
pipeline_tag: text2text-generation
---

## 🧠 Fine-tuned CodeLlama on LeetCode Problems

**This model is a fine-tuned version of [`codellama/CodeLlama-7b-Instruct-hf`](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) on the [`greengerong/leetcode`](https://huggingface.co/datasets/greengerong/leetcode) dataset. It has been instruction-tuned to generate Python solutions from LeetCode-style problem descriptions.**
---

## 📦 Model Formats Available

- **Transformers-compatible (`.safetensors`)** — for use via 🤗 Transformers.
- **GGUF (`.gguf`)** — for use via [llama.cpp](https://github.com/ggerganov/llama.cpp), including `llama-server`, `llama-cpp-python`, and other compatible tools.

---

## 🔗 Example Usage (Transformers)

```python
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

model_id = "harshism1/codellama-leetcode-finetuned"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = """You are an AI assistant. Solve the following problem:

Given an array of integers, return indices of the two numbers such that they add up to a specific target.

## Solution
"""

result = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7)
print(result[0]["generated_text"])
```



## ⚙️ Usage with `llama.cpp`

You can run the model using tools in the [`llama.cpp`](https://github.com/ggerganov/llama.cpp) ecosystem. Make sure you have the `.gguf` version of the model (e.g., `codellama-leetcode.gguf`).

### 🐍 Using `llama-cpp-python`

Install:

```bash
pip install llama-cpp-python
```
Then use:

```
from llama_cpp import Llama

llm = Llama(
    model_path="codellama-leetcode.gguf",
    n_ctx=4096,
    n_gpu_layers=99  # adjust based on your GPU
)

prompt = """### Problem
Given an array of integers, return indices of the two numbers such that they add up to a specific target.

## Solution
"""

output = llm(prompt, max_tokens=256)
print(output["choices"][0]["text"])
```


### 🖥️ Using llama-server

Start the server:

```
llama-server --model codellama-leetcode.gguf --port 8000 --n_gpu_layers 99
```

Then send a request:

```
curl http://localhost:8000/completion -d '{
  "prompt": "### Problem\nGiven an array of integers...\n\n## Solution\n",
  "n_predict": 256
}'
```