Instructions to use FINAL-Bench/Darwin-28B-Coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FINAL-Bench/Darwin-28B-Coder with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="FINAL-Bench/Darwin-28B-Coder")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("FINAL-Bench/Darwin-28B-Coder")
model = AutoModelForImageTextToText.from_pretrained("FINAL-Bench/Darwin-28B-Coder")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use FINAL-Bench/Darwin-28B-Coder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FINAL-Bench/Darwin-28B-Coder"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FINAL-Bench/Darwin-28B-Coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FINAL-Bench/Darwin-28B-Coder

SGLang

How to use FINAL-Bench/Darwin-28B-Coder with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "FINAL-Bench/Darwin-28B-Coder" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FINAL-Bench/Darwin-28B-Coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "FINAL-Bench/Darwin-28B-Coder" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FINAL-Bench/Darwin-28B-Coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FINAL-Bench/Darwin-28B-Coder with Docker Model Runner:
```
docker model run hf.co/FINAL-Bench/Darwin-28B-Coder
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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