---
license: other
base_model: unsloth/Qwen3-8B-bnb-4bit
tags:
  - solidity
  - smart-contracts
  - vulnerability-detection
  - security
  - code-security
  - ethereum
  - qwen3
  - awq
  - compressed-tensors
library_name: transformers
---

# Model Alpha — Qwen3-8B fine-tuned for Solidity vulnerability detection

## Overview

Model Alpha is a fine-tuned version of [Qwen3-8B](https://huggingface.co/unsloth/Qwen3-8B-bnb-4bit) specialized in identifying high-severity vulnerabilities in Solidity smart contracts. It is served as an OpenAI-compatible endpoint and powers a public audit pipeline at `https://openai.vast.ai/model-alpha`.

The weights released here are the same AWQ pack-quantized int4 weights running in production, byte-identical to the deployed endpoint. The format is `compressed-tensors` (`pack-quantized`, W4A16, group_size=128).

## Training data

Trained on approximately **3,291 labelled examples** (2,961 train / 330 test) drawn from public smart contract audits across the following categories:

- **DeFi protocols** — lending markets, DEXs (AMM + order-book), staking, vaults, yield aggregators
- **NFT and gaming** — marketplaces, royalty engines, on-chain randomness, claim flows
- **Cross-chain bridges** — message passing, signature verification, replay protection
- **Oracles and price feeds** — TWAP, Chainlink integration, fallback handling
- **Account abstraction and wallets** — session keys, paymasters, signature replay
- **Stablecoins and synthetic assets** — mint/burn, collateral, liquidation
- **Governance and DAOs** — voting, timelock, proposal execution

Examples were constructed by combining function-level Solidity source with structured call-graph context and labelled by audit methodology (vulnerability type, severity, dollar impact where applicable).

## Intended use

- Smart contract security auditing (defensive / white-hat)
- Vulnerability triage during code review
- Research on LLM-based code analysis for adversarial code

Not intended for: identifying exploits for offensive use, generating malicious contracts, or auditing contracts outside EVM-compatible chains.

## Evaluation

### Wake Arena v8 (94 high-severity vulnerabilities, Ackee Blockchain)

| Setting | Result |
|---|---|
| **Primary function only, 3 samples, 2/3 majority vote** | **69 / 94 = 73.4%** |
| All sibling functions, 3 samples, majority (ceiling) | 88 / 94 = 93.6% |
| Pashov skill (published external measurement, @0xTomass) | 49 / 94 = 52.1% |
| Opus 4.7 MAX (same external measurement) | 48 / 94 = 51.1% |

Methodology documented in `evaluation_results/wake_arena/`. Same-model Alpha scores range 21.3% → 93.6% depending on prompt format and sample count.

### evmbench (117 high-severity vulnerabilities, OpenAI Frontier Evals)

| Metric | Alpha | Claude Fable | GLM-5.1 |
|---|---:|---:|---:|
| Detected | 6 / 117 | 16 / 117 | 15 / 117 |
| Total $ award on detected | **$20,691** | $2,422 | $646 |

Alpha's single highest-value catch — **Sequence H-02 ($20,367)** — was a partial-signature replay vulnerability that neither Fable nor GLM-5.1 detected under the same pipeline. That single find is worth 8.4× Fable's entire 16-catch portfolio. Source: `evmbench_full_alpha_20260610_021252.json`.

## Specifications

- **Base model:** unsloth/Qwen3-8B-bnb-4bit
- **Architecture:** Qwen3ForCausalLM (36 layers, hidden_size 4096)
- **Context length:** 16,384 tokens (production), up to 40,960 (architecture max)
- **Quantization:** AWQ W4A16, pack-quantized, group_size=128, via `compressed-tensors`
- **Disk size:** ~5.7 GB
- **Serving:** vLLM with `--quantization compressed-tensors`

## Usage

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "l-lyubenov/model-alpha"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

prompt = """Audit this Solidity function for high-severity vulnerabilities:

function withdraw(uint256 amount) external {
    uint256 balance = balances[msg.sender];
    require(amount <= balance, "insufficient");
    (bool ok, ) = msg.sender.call{value: amount}("");
    require(ok, "transfer failed");
    balances[msg.sender] = balance - amount;
}
"""
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, do_sample=False)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

Or via vLLM:

```bash
vllm serve l-lyubenov/model-alpha \
  --quantization compressed-tensors \
  --max-model-len 16384 \
  --gpu-memory-utilization 0.90
```

## Limitations

- Trained primarily on EVM-compatible Solidity; performance on Vyper, Move, Cairo is unknown
- May produce false positives on safe patterns (assembly blocks, low-level calls, deliberate reentrancy)
- High flag rate with moderate precision — recommended as a triage tool, not a final arbiter
- Single-turn chat only; not optimized for multi-turn code review dialogue

## License

Public release — weights are available to anyone with a Hugging Face account. Use is restricted to **defensive security purposes** (auditing, vulnerability research on authorized systems, academic study, tooling that identifies or remediates vulnerabilities). Commercial use, redistribution as a hosted service, and offensive use are **not granted** by this release. See LICENSE for the full terms.

## Citation

If you use Model Alpha in published work, please cite the public benchmark results:

```
@software{model_alpha_2026,
  title  = {Model Alpha: Qwen3-8B fine-tuned for Solidity vulnerability detection},
  author = {Lyubenov, Lyuboslav},
  year   = {2026},
  url    = {https://huggingface.co/l-lyubenov/model-alpha}
}
```