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README.md
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---
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datasets:
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- greengerong/leetcode
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language:
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- en
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base_model:
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- codellama/CodeLlama-7b-Instruct-hf
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pipeline_tag: text2text-generation
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---
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## 🧠 Fine-tuned CodeLlama on LeetCode Problems
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**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.**
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---
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## 📦 Model Formats Available
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- **Transformers-compatible (`.safetensors`)** — for use via 🤗 Transformers.
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- **GGUF (`.gguf`)** — for use via [llama.cpp](https://github.com/ggerganov/llama.cpp), including `llama-server`, `llama-cpp-python`, and other compatible tools.
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---
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## 🔗 Example Usage (Transformers)
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
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model_id = "your-username/codellama-leetcode-finetuned"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
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prompt = """You are an AI assistant. Solve the following problem:
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Given an array of integers, return indices of the two numbers such that they add up to a specific target.
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## Solution
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"""
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result = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7)
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print(result[0]["generated_text"])
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```
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## ⚙️ Usage with `llama.cpp`
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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`).
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### 🐍 Using `llama-cpp-python`
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Install:
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```bash
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pip install llama-cpp-python
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```
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Then use:
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```
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from llama_cpp import Llama
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llm = Llama(
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model_path="codellama-leetcode.gguf",
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n_ctx=4096,
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n_gpu_layers=99 # adjust based on your GPU
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)
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prompt = """### Problem
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Given an array of integers, return indices of the two numbers such that they add up to a specific target.
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## Solution
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"""
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output = llm(prompt, max_tokens=256)
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print(output["choices"][0]["text"])
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```
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### 🖥️ Using llama-server
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Start the server:
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```
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llama-server --model codellama-leetcode.gguf --port 8000 --n_gpu_layers 99
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```
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Then send a request:
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```
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curl http://localhost:8000/completion -d '{
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"prompt": "### Problem\nGiven an array of integers...\n\n## Solution\n",
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"n_predict": 256
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}'
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```
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