Instructions to use coronerai/coroner-coder-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use coronerai/coroner-coder-7b with Transformers:

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

pipe = pipeline("text-generation", model="coronerai/coroner-coder-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("coronerai/coroner-coder-7b")
model = AutoModelForCausalLM.from_pretrained("coronerai/coroner-coder-7b", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use coronerai/coroner-coder-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "coronerai/coroner-coder-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "coronerai/coroner-coder-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/coronerai/coroner-coder-7b

SGLang

How to use coronerai/coroner-coder-7b 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 "coronerai/coroner-coder-7b" \
    --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": "coronerai/coroner-coder-7b",
		"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 "coronerai/coroner-coder-7b" \
        --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": "coronerai/coroner-coder-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use coronerai/coroner-coder-7b with Docker Model Runner:
```
docker model run hf.co/coronerai/coroner-coder-7b
```

Coroner Coder 7B

A small model that fixes the patterns the Coroner scanner flags in an ERC-20 token contract: it takes a Solidity source plus the scanner's flags and returns a corrected contract and a short list of changes.

Always compile and review the result yourself. A patch that compiles is not an audit. Not financial or security advice.

What it fixes

The same patterns the Coroner pre-mortem scanner reports from source code: mint_with_live_owner, blacklist_function, pause_function, tax_changeable, risky_opcode (selfdestruct, delegatecall, tx.origin) and owner_privileges.

Training

Base: Qwen2.5-Coder-7B-Instruct (Apache 2.0). Method: LoRA (rank 16) on 4-bit weights, one epoch (88 steps), then merged to 16-bit.
Data: 1,400 synthetic examples. Each is a flawed token contract with its fix, generated from templates in two source layouts. Every target is verified: the corrected contract compiles with solc 0.8.24, keeps the ERC-20 interface and clears the flagged patterns. Generator and verifier: https://github.com/coroner-hash/ai/tree/main/coder

Evaluation

A fix counts only if the patched contract compiles, keeps the ERC-20 interface, clears every flag and adds none. 40 held-out contracts in a third source layout the model never saw in training (greedy decoding):

	Base model	Coroner Coder
Compiles	1.00	0.925
Flags cleared	0.125	0.925
Full fix (all checks)	0.125	0.925

Returning the contract unchanged scores 0.05. In the layouts it was trained on the model scores 1.00, which mostly shows it learned the templates.

Limits

Synthetic, narrow data. All contracts are single-file ERC-20 variants without imports. Real contracts from a market will be messier; expect lower numbers. This is a first version.
About 1 in 13 outputs did not compile in the held-out test. Always compile.
It removes risky features; it does not find new bugs, and it can change behaviour. Review the diff.
Only the scanner's six patterns. Not a general Solidity auditor.

Source, data generator and scorer: https://github.com/coroner-hash/ai

Downloads last month: 6

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for coronerai/coroner-coder-7b

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Coder-7B

Finetuned

Qwen/Qwen2.5-Coder-7B-Instruct

Adapter

(823)

this model

Adapters

1 model