Instructions to use i-Coder/iCoder-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use i-Coder/iCoder-27B with Transformers:

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

pipe = pipeline("text-generation", model="i-Coder/iCoder-27B")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("i-Coder/iCoder-27B")
model = AutoModelForMultimodalLM.from_pretrained("i-Coder/iCoder-27B", device_map="auto")
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 Settings

vLLM

How to use i-Coder/iCoder-27B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "i-Coder/iCoder-27B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "i-Coder/iCoder-27B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/i-Coder/iCoder-27B

SGLang

How to use i-Coder/iCoder-27B 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 "i-Coder/iCoder-27B" \
    --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": "i-Coder/iCoder-27B",
		"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 "i-Coder/iCoder-27B" \
        --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": "i-Coder/iCoder-27B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use i-Coder/iCoder-27B with Docker Model Runner:
```
docker model run hf.co/i-Coder/iCoder-27B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

iCoder-27B

iCoder-27B is a 27B-parameter model for industrial coding, covering RTL design and GPU kernel optimization.

It is the product of an experiment in delegating model development itself. Human experts encoded their model-development experience once, as reusable Research Skills. From that point on an agent instantiated those Skills, allocated resources, ran and diagnosed experiments, and revised the training strategy. The agent coordinated a multi-stage pipeline spanning supervised fine-tuning, on-policy self-distillation, and reinforcement learning with verifiable rewards, in which every reward comes from compiling and running the model's own output rather than from comparison against a reference text.

Despite its compact scale, iCoder-27B surpasses models with up to 59x more total parameters, including DeepSeek-V4-Pro, GLM-5.2 and Kimi-K2.6. It leads on RTLLM (68.0), ties Claude Opus 4.8 for the best TritonBench-G pass@1 (20.1), and ranks second on KernelBench L2 Fast and on CVDP. Its 61% KernelBench L1 correctness is the highest of any model evaluated.

The full technical report describing the recipe is available here.

Results

Every model is evaluated through the same harness. RTL benchmarks run under the simulator each official suite specifies; kernel benchmarks compare candidate outputs against the reference implementation under matched inputs. Bold marks the best result in each row and italic the second best.

Benchmark	Metric	iCoder-27B	Qwen3.6-27B	InCoder-32B	InCoder-32B-T	DeepSeek-V4-Pro	GLM-5.2	Kimi-K2.6	GPT-5.5	Claude-Opus-4.8	Hy3	Gemini-3.5-Flash
VerilogEval	Spec-to-RTL avg@4	86.3	70.1	62.5	65.9	69.9	66.0	72.4	90.1	82.7	83.8	89.1
VerilogEval	Code-complete avg@4	86.0	70.8	58.2	54.2	79.8	74.8	78.5	91.4	81.9	81.6	83.8
RTLLM	Functional avg@4	68.0	49.6	48.0	44.2	67.5	64.0	59.0	66.0	64.7	53.5	63.5
CVDP	Functional avg@5 (%)	44.1	33.9	36.9	30.3	38.5	39.5	42.1	39.5	47.7	39.7	29.7
RealBench	Syntax pass@5 (%)	61.7	38.3	60.0	55.0	36.7	43.3	58.3	80.0	83.3	41.7	68.3
RealBench	Functional pass@5 (%)	26.7	16.7	46.7	36.7	16.7	25.0	25.0	28.3	36.7	16.7	26.7
ArchXBench	Functional pass@1 (%)	49.3	35.2	36.6	29.6	50.7	50.7	42.3	56.3	54.9	47.9	50.7
KernelBench L1	Compiled (%)	95	87	88	85	93	96	93	98	95	94	94
KernelBench L1	Correct (%)	61	32	51	47	32	50	32	43	55	42	45
KernelBench L1	Fast (%)	25	12	18	18	13	26	5	22	30	21	23
KernelBench L2	Compiled (%)	97	89	90	93	91	98	84	100	97	98	99
KernelBench L2	Correct (%)	74	28	65	63	40	40	17	41	70	56	78
KernelBench L2	Fast (%)	40	17	14	15	25	30	7	24	37	29	47
KernelBench L3	Compiled (%)	90	86	60	60	86	90	82	100	84	98	100
KernelBench L3	Correct (%)	34	12	30	20	4	30	18	38	40	18	58
KernelBench L3	Fast (%)	10	4	14	12	2	0	0	6	8	2	14
TritonBench-G	Correctness pass@1 (%)	20.1	11.4	17.9	18.5	19.0	19.0	19.0	19.5	20.1	19.5	14.9

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "i-Coder/iCoder-27B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, dtype="auto", device_map="auto"
)

messages = [{"role": "user", "content": "Write a 4-bit synchronous up counter with active-low reset in Verilog."}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=2048)
print(tokenizer.decode(out[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True))

Citation

If you find iCoder useful in your work, please cite the technical report:

@techreport{yang2026icoder,
  title     = {iCoder-27B: Recursive AI-Led Development of Frontier Industrial Coding Model},
  author    = {Cheng Yang and Jiayang Lyu and Shangyuan Liu and Guibin Zhang and
               Jiong Lin and Xinlei Yu and Junchi Yan and Shuicheng Yan and
               Weinan E and Linfeng Zhang and Linfeng Zhang and Qibing Ren},
  year      = {2026},
  month     = aug,
  type      = {Technical Report},
  url       = {https://huggingface.co/i-Coder/iCoder-27B}
}

License

Apache-2.0, inherited from the base model, Qwen3.6-27B.

Downloads last month: 147

Safetensors

Model size

27B params

Tensor type

BF16

Model tree for i-Coder/iCoder-27B

Base model

Qwen/Qwen3.6-27B

Finetuned

(375)

this model

Quantizations

2 models