Top Open-Source Coding LLMs Revolutionizing Development

The rise of open-source large language models (LLMs) has significantly impacted the developer community, offering powerful tools for code generation and natural language tasks. In this article, we explore the top coding-focused LLMs that are making waves, highlighting their unique features, performance benchmarks, and available models.

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1. Mistral 7B & Mixtral 8X7B

Developed by Mistral AI and released under the Apache 2.0 license, Mistral 7B and Mixtral 8X7B are state-of-the-art models tailored for efficiency and performance.

Mistral 7B

• Parameters: 7.3B
• Key Features:
  • Outperforms Llama 2 13B on most benchmarks and rivals CodeLlama 7B in coding tasks.
  • Efficiently handles longer sequences with Sliding Window Attention (SWA).
  • Incorporates Grouped Query Attention (GQA) for faster inference.

Mixtral 8X7B

• Parameters: 46.7B Sparse Mixture-of-Experts (SMoE) model, effectively using 12.9B per token.
• Key Features:
  • Matches or surpasses Llama 2 70B in benchmarks.
  • Balances high parameter count with cost-effective processing.

Both models excel in code generation and instruction fine-tuning. For example, Mistral 7B Instruct demonstrates top-tier performance on benchmarks like MT-Bench.

Models Available:

• Mistralai/Mistral-7B-v0.1
• Mistralai/Mixtral-8x7B-v0.1
• Fine-tuned variations: Instruct models for specialized tasks.

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2. CodeLlama by Meta

Built on Llama 2, CodeLlama is a versatile model designed for code-related tasks.

Variants:

1. CodeLlama: Foundational model.
2. CodeLlama - Python: Optimized for Python.
3. CodeLlama - Instruct: Fine-tuned for understanding natural language instructions.

Training:

• Sizes: 7B, 13B, 34B, and 70B parameters.
• Tokens:
  • 7B, 13B, and 34B trained on 500B tokens.
  • 70B trained on 1T tokens.

Performance:

• Benchmarks:
  • HumanEval: CodeLlama 34B scores 53.7%.
  • MBPP: Scores 56.2%, outperforming many open-source solutions.

Models Available:

• Variants include Instruct, Python, and standard models for all sizes.

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3. Phind-CodeLlama

Phind has fine-tuned CodeLlama models for exceptional coding task performance.

Key Achievements:

• Phind-CodeLlama-34B-v1: Scores 67.6% on HumanEval.
• Phind-CodeLlama-34B-v2: Improves to 73.8% pass@1.

Fine-Tuning Process:

• Optimized over high-quality instruction-answer pairs.
• Training executed with DeepSpeed ZeRO 3 and Flash Attention 2.

Models Available:

• Phind-CodeLlama-34B-v1
• Phind-CodeLlama-34B-v2

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4. StarCoder & StarCoder2

StarCoder models, developed by the BigCode project, are specialized for responsible and open development.

StarCoder:

• Parameters: 15.5B
• Dataset: Trained on 1T tokens from The Stack.

StarCoder2:

• Dataset: Trained on a 4× larger dataset (The Stack v2).
• Variants: 3B, 7B, and 15B models.

Models Available:

• StarCoder2-15B
• StarCoder2-7B
• StarCoder

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5. WizardCoder

WizardCoder enhances StarCoder through the Evol-Instruct fine-tuning method, generating diverse and challenging instructions.

Models Available:

• WizardCoder-Python-34B-V1.0
• WizardCoder-15B-V1.0

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6. Solar 10.7B

SOLAR 10.7B is a lightweight yet powerful model initialized from Mistral 7B.

Fine-Tuning:

• Two stages: Instruction Tuning and Alignment Tuning.
• Demonstrates superior performance over larger models like Mixtral 8X7B.

Models Available:

• Solar-10.7B-Instruct

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Conclusion

Open-source coding LLMs like Mistral, CodeLlama, Phind, StarCoder, WizardCoder, and Solar are reshaping how developers approach code generation and related tasks. With cutting-edge fine-tuning techniques, these models deliver robust, scalable solutions for modern programming challenges.