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

Libraries

How to use actuaryai/coder-7b with Transformers:

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

pipe = pipeline("text-generation", model="actuaryai/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("actuaryai/coder-7b")
model = AutoModelForCausalLM.from_pretrained("actuaryai/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 actuaryai/coder-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "actuaryai/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": "actuaryai/coder-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/actuaryai/coder-7b

SGLang

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

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

coder-7b / README.md

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---
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
library_name: transformers
pipeline_tag: text-generation
tags:
  - solidity
  - smart-contracts
  - token-risk
  - lora
language:
  - en
---
![Actuary: price a token's life before your agent spends on it](https://actuaryai.pro/og.png)

# Actuary coder-7B

A small model that fixes the patterns the [Actuary](https://actuaryai.pro) 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 Actuary 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 | coder-7B |
|---|---|---|
| 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 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.

## Links
- Actuary: https://actuaryai.pro · X: https://x.com/useActuary
- Source, data generator and scorer: https://github.com/coroner-hash/ai

	---
	license: apache-2.0
	base_model: Qwen/Qwen2.5-Coder-7B-Instruct
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- solidity
	- smart-contracts
	- token-risk
	- lora
	language:
	- en
	---

	![Actuary: price a token's life before your agent spends on it](https://actuaryai.pro/og.png)

	# Actuary coder-7B

	A small model that fixes the patterns the [Actuary](https://actuaryai.pro) 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 Actuary 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 \| coder-7B \|
	\|---\|---\|---\|
	\| 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 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.

	## Links
	- Actuary: https://actuaryai.pro · X: https://x.com/useActuary
	- Source, data generator and scorer: https://github.com/coroner-hash/ai