harshism1/codellama-leetcode-finetuned

🧠 Fine-tuned CodeLlama on LeetCode Problems

This model is a fine-tuned version of codellama/CodeLlama-7b-Instruct-hf on the 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, including llama-server, llama-cpp-python, and other compatible tools.

---

🔗 Example Usage (Transformers)

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 ecosystem. Make sure you have the .gguf version of the model (e.g., codellama-leetcode.gguf).

🐍 Using llama-cpp-python

Install:

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
}'