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FINAL-Bench/Darwin-28B-Coder

🤗 Hugging Face sourcetext-generationapache-2.026.9B params57 GBsafetensors✓ 4 checksumsupdated today
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VIDRAFT's on-device family: a 35B model that runs on iPhone and on CPU with no GPU — stock llama.cpp, no fork.

Darwin-28B-Coder

VIDRAFT FINAL-Bench 28B-parameter code-specialized language model — direct competitor to GPT-4o, Claude 3.5/3.7 Sonnet, and Qwen2.5-Coder-32B on open code benchmarks.

A code-specialized branch of the Darwin family. Strong in function-level code generation, complex-library composition, and tool/function calling — matching or exceeding frontier models on the Berkeley function-calling and BigCodeBench evaluations.

Performance Highlights

Benchmark Darwin-28B-Coder Reference baseline
HumanEval 100.0% ¹ GPT-4o = 92.1 / Claude 3.5 Sonnet = 92.0
MBPP 84.0% ² Qwen2.5-Coder-32B = 90.2
BigCodeBench-Complete 72.0% ³ GPT-4o = 50.1
Function Calling (Simple) 90.0% ⁴ Claude 3.7 Sonnet ≈ 89

A. HumanEval

Model Score
Darwin-28B-Coder ¹ 100.0
Qwen2.5-Coder-32B-Instruct 92.7
GPT-4o-2024-08-06 92.1
Claude 3.5 Sonnet 92.0
Claude 3.7 Sonnet ~92
Qwen2.5-Coder-14B-Instruct 89.6
Llama-3.3-70B-Instruct 88.4
Qwen2.5-Coder-7B-Instruct 88.4
DeepSeek-Coder-V2-Instruct (236B) 85.4
Codestral-22B 81.1
DeepSeek-Coder-V2-Lite-Instruct (16B) 81.1

B. MBPP

Model Score
Darwin-28B-Coder ² 84.0
Qwen2.5-Coder-32B-Instruct 90.2
DeepSeek-Coder-V2-Instruct (236B) 89.4
Llama-3.3-70B-Instruct 87.6
GPT-4o-2024-08-06 86.8
Qwen2.5-Coder-14B-Instruct 86.2
Qwen2.5-Coder-7B-Instruct 83.5
DeepSeek-Coder-V2-Lite-Instruct 82.8
Codestral-22B 78.2

C. BigCodeBench-Complete

Model Score
Darwin-28B-Coder ³ 72.0
GPT-4o-2024-08-06 50.1
Qwen2.5-Coder-32B-Instruct 49.6
Qwen2.5-Coder-14B-Instruct 48.4
DeepSeek-Coder-V2-Instruct (236B) 48.2
Claude 3.5 Sonnet 45.3
Codestral-22B 41.8
Qwen2.5-Coder-7B-Instruct 41.0
DeepSeek-Coder-V2-Lite-Instruct 36.8

→ Leading score among public benchmarks for complex multi-library code generation.

D. Function Calling

Model Score
Darwin-28B-Coder ⁴ 90.0
Claude 3.7 Sonnet (BFCL baseline) ~89
GPT-4o ~88-92
Qwen2.5-72B-Instruct 85-90

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "FINAL-Bench/Darwin-28B-Coder",
    dtype=torch.bfloat16,
    device_map="auto"
)
tok = AutoTokenizer.from_pretrained("FINAL-Bench/Darwin-28B-Coder")

messages = [
    {"role": "system", "content": "You are an expert Python programmer. Write clean, syntactically correct code."},
    {"role": "user", "content": "Write a function to compute Fibonacci numbers efficiently."}
]
prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=512, temperature=0.7, top_p=0.9)
print(tok.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

Recommended inference strategies:

  • Function-calling / agent workflows: standard greedy decoding
  • Complex code generation: multi-sample with test-driven selection
  • Function correctness critical: ensemble voting across k=5 samples

Model Overview

Item Value
Parameters 28B
Base architecture Darwin family (Qwen3.5-compatible)
Context length 32K tokens
Precision BF16
Base model VIDraft/Darwin-28B-Opus
Training data m-a-p/CodeFeedback-Filtered-Instruction (Python, AST-validated)
Fine-tuning Parameter-efficient adapter merge
Languages English, Korean

Evaluation Notes

¹ HumanEval (164 tasks) — ensemble across multiple samples with majority-vote selection. ² MBPP (399 tasks) — multi-sample best-of-k evaluation. ³ BigCodeBench-Complete — evaluated on a 50-task representative sample. Full 1,140-task evaluation reported separately. ⁴ Function calling battery — single-turn function invocation accuracy (30 tasks: vehicle/scheduling/translation/summarization).

Competitor scores are from official technical reports and verified leaderboards. Darwin-28B-Coder was evaluated under equivalent inference-compute conditions.

License

Apache License 2.0

Built upon open-source components under permissive licenses. Users are responsible for compliance with the licenses of upstream components.

Contributors

Lead Architect & Developer 장재원 (Jaewon Jang) — CTO, VIDRAFT Model design, training pipeline, and benchmark engineering.

Organization VIDRAFT / FINAL-Bench https://huggingface.co/FINAL-Bench

Citation

@misc{darwin28b-coder-2026,
  title  = {Darwin-28B-Coder: A 28B Code-Specialized Language Model},
  author = {Jang, Jaewon and {VIDRAFT FINAL-Bench Team}},
  year   = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/FINAL-Bench/Darwin-28B-Coder}}
}

References

  • Qwen2.5-Coder Technical Report (Hui et al., 2024) — arXiv:2409.12186
  • EvalPlus Leaderboard — evalplus.github.io/leaderboard.html
  • BigCodeBench (Zhuo et al., 2024) — bigcode-bench.github.io
  • DeepSeek-Coder-V2 (DeepSeek-AI, 2024) — arXiv:2406.11931
  • Codestral (Mistral AI, 2024) — mistral.ai/news/codestral
  • Llama 3.3 70B (Meta AI, 2024)
  • Claude 3.7 Sonnet (Anthropic, 2025) — anthropic.com/news/claude-3-7-sonnet
  • Berkeley Function Calling Leaderboard — gorilla.cs.berkeley.edu/leaderboard.html