DeepCoder a fully open-source 14B coder Overview

What is DeepCoder-14B-Preview?

The Agentica and Together AI teams built this open-source code generation model. DeepCoder-14B-Preview solves tough programming challenges and serves as a strong alternative to OpenAI’s o3-mini model.

DeepCoder a fully open-source 14B coder Overview

Performance and Results

• Pass@1 accuracy on LiveCodeBench: DeepCoder-14B-Preview reached a 60.6% accuracy rate. Its performance matches with o3-mini‘s 60.9%, despite having just 14 billion parameters.
• Codeforces ranking: It ranked 1936, which puts it among the top 5% of human programmers.

Training and Data

• Training data: The team relied on a collection of verified programming challenges sourced from places like TACO, SYNTHETIC-1, and LiveCodeBench.
• Training techniques: They used reinforcement learning with rewards that could be verified. This approach helped sharpen the model’s problem-solving abilities for programming tasks.
• Technical improvements: A verl-pipe system was introduced to make the training both faster and more efficient.

Open Source

They made everything available open source, which includes:

• The dataset they used.
• The code for training.
• Logs from training sessions.
• Details about system advancements.

Check them out using these links:

• Hugging Face Model
• Hugging Face Dataset
• GitHub
• Wandb
• Eval Logs

Uses and Applications

Developers can rely on DeepCoder-14B-Preview in numerous programming scenarios such as:

• Writing and generating code.
• Reviewing and making code better.
• Tackling hard programming challenges.

Its strong performance makes it a valuable resource to assist developers and artificial intelligence researchers.

Technical info and key features of DeepCoder-14B-Preview:

Design and technical background:

1. Model Structure:
   • It follows the Transformer design found in GPT models.
   • The model includes 14 billion parameters.
   • Developers trained the model to handle Codex-style prompts. It processes and creates code by following instructions written in human language.
2. Reward System with Smarts:
   • The team used Reinforcement Learning with Feedback (RLHF). They made it better by tweaking how rewards worked.
   • Rewards came from how well the model could pass unit tests.
   • This approach helped the model improve its performance instead of just memorizing stuff.
3. Verl-Pipe Parallel Training Framework:
   • Verl-Pipe is a fresh method to train models.
   • It uses less memory.
   • It shares tasks among several GPUs.
   • It makes training faster compared to older methods.

Performance Compared to Competitors

What sets DeepCoder apart is that it achieves performance close to o3-mini with fewer resources and is completely open source.

Community and Contribution

The project is fully open to the community:

• Developers can download the model from Hugging Face.
• The code is available on GitHub.
• A complete log file of all experiments and evaluations is available on Google Drive.
• The model is scalable and improveable by anyone.

Important Links

• Download the model on Hugging Face
• Source code on GitHub
• Training experiments and results (Wandb)
• Data files and evaluation

Who is this model for?

• Researchers in generative AI.
• Developers who need an intelligent programming assistant.
• Product building teams interested in automated programming solutions.
• Students or universities who want to study powerful AI models without licensing restrictions.

A step-by-step explanation of how to actually use the DeepCoder-14B model

Step 1: Install the appropriate environment

You need:

• Python 3.10 or higher
• Libraries: transformers, accelerate, torch, and datasets

Installation:

pip install transformers accelerate torch datasets

Step 2: Load the model from Hugging Face

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "deepcoder-ai/deepcoder-14b-preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype="auto")
    

Step 3: Try generating code

A simple example of generating a Python function that adds two numbers:

prompt = "Write a Python function that adds two numbers:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
    

Step 4: Optimize the results (Optional)

To optimize the resulting code:

• Use explicit instructions like:
• # Function to check if a number is prime
• # Complete the function below
• You can use Temperature and Top-p Sampling for greater variety:

outputs = model.generate(
    **inputs,
    max_new_tokens=100,
    do_sample=True,
    top_p=0.95,
    temperature=0.7
)
    

Pro Tips

• If you’re working on a large project, use batch inference to generate multiple tokens at once.
• Be sure to test the resulting code, as it may sometimes be slightly inaccurate.
• The model is good at the following tasks:
• Solving LeetCode problems.
• Generating Python, JavaScript, and C++ functions.
• Rewriting code more cleanly.

Can the model be fine-tune?

Yes! DeepCoder is available for modification and retraining, and you can train it on your own projects using Hugging Face’s Trainer or tools like LoRA to reduce costs.

If you want to know more about DeepCoder-14B-Preview, you can visit the website for more details.