Instructions to use samscrack/Qwen3.6-Solidity-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use samscrack/Qwen3.6-Solidity-27B with Transformers:

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

pipe = pipeline("text-generation", model="samscrack/Qwen3.6-Solidity-27B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("samscrack/Qwen3.6-Solidity-27B")
model = AutoModelForCausalLM.from_pretrained("samscrack/Qwen3.6-Solidity-27B")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use samscrack/Qwen3.6-Solidity-27B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "samscrack/Qwen3.6-Solidity-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": "samscrack/Qwen3.6-Solidity-27B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/samscrack/Qwen3.6-Solidity-27B

SGLang

How to use samscrack/Qwen3.6-Solidity-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 "samscrack/Qwen3.6-Solidity-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": "samscrack/Qwen3.6-Solidity-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 "samscrack/Qwen3.6-Solidity-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": "samscrack/Qwen3.6-Solidity-27B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use samscrack/Qwen3.6-Solidity-27B with Docker Model Runner:
```
docker model run hf.co/samscrack/Qwen3.6-Solidity-27B
```

Qwen 3.6 Solidity (27B)

A 5-stage Solidity-specialist fine-tune of Qwen/Qwen3.6-27B. Trained to produce Foundry-compileable Solidity contracts and matching test suites from natural- language specs, and to reason about smart-contract security with long-CoT audit traces.

This is the final merged checkpoint — all five stages (CPT → SFT instruction → SFT audit/CoT → SFT Opus distillation → RFT) folded into a single bf16 model. Loadable directly with AutoModelForCausalLM.from_pretrained(...) — no adapters to apply.

Solidity Eval (2026) — pass@1 leaderboard

Top of the pass@1 leaderboard on samscrack/solidity-eval-2026 (lite split, 200 real Etherscan contracts):

Agent / model	pass@1	Wall-clock
This model — Qwen 3.6 Solidity 27B	46.5% (93/200)	~27 min
Claude Code 2.1.128 (Claude Opus 4.7)	39.0% (78/200, 1 timeout)	~34 min

pass@1 here is SolBench's echidna() rule: a single agentic attempt is scored 1.0 only if Diffusc compiles the candidate AND Echidna's differential-fuzz finds no behavioral divergence vs. the ground-truth body, with B3 canary + stub-residue guards. No resampling. Identical conditions across rows: 16-way concurrency, max_agent_turns=40, agent_temperature=0.6, fuzz_test_calls=50000, fuzz_seed=0xDEADBEEF, same sandbox image, same host. This model served locally via vLLM TP=2 FP8 (qwen3_xml tool parser) on 2× Blackwell GPUs through the in-process Hermes agent loop; Claude Code via Anthropic API through the CLI agent backend.

See the dataset card for the full reproduction recipe and harness-agnostic scoring instructions.

Pipeline

#	Stage	Method	Adapter	Training data
0	Continued pretrain	LoRA r=64, ~500M Solidity tokens	folded in	`ASSERT-KTH/DISL` (514k deployed contracts, CC-BY 4.0) + ~80 curated blue-chip GitHub repos
1B	Instruction SFT	LoRA r=64, 178 steps	folded in	`final.jsonl` (~315k rows: braindao/solidity-base-sft-v2 + andstor/smart_contract_code_comments + lohoz/Smart-Contract-MultiTask + slither-audited + Pyano-fun) + 4,240 unverified `foundry_tests.jsonl` rows
2	Audit / long-CoT	LoRA r=16, 2 epochs	folded in	`samscrack/solidity-audit-cot` (~6,140 Opus 4.7 long-form audit traces, all `confidence=high`, ≤30k chars to fit 8K ctx)
3	Opus distillation SFT	LoRA r=16, 2 epochs, lr=5e-5	folded in	4,000 of 4,919 forge-verified Opus pairs (`foundry_tests.verified.jsonl`); 919 held out from training
4	Rejection fine-tuning (RFT)	LoRA r=16, 2 epochs, lr=5e-5	folded in (this checkpoint)	926 model-generated contract+test pairs that passed `forge build && forge test` self-oracle, with non-triviality gate (≥3 test fns, ≥2 distinct asserts)

Stages 0/1B/2 were the original recipe (specification + Opus-CoT distillation). Stages 3/4 are the addition: directly distill the highest-quality forge-verified Opus pairs (Stage 3), then rejection-sample the model's own forge-passing outputs to anchor self-consistent generation (Stage 4).

Eval — Stage 3 → Stage 4 (RFT) comparison

200 prompts × N=4 candidates from a held-out slice (never trained on at any stage). Each model-generated (contract, test_file) pair is dropped into a fresh Foundry project and scored end-to-end with forge build && forge test:

Metric (200 prompts × N=4 candidates)	Post-Stage-3	Post-Stage-4	Δ
extract success	80.5%	86.4%	+5.9 pp
compile success	46.8%	50.6%	+3.8 pp
test pass	19.2%	21.4%	+2.2 pp
prompts ≥1 pass	45.0%	54.0%	+9.0 pp

Stage 4 RFT lifted prompt-level yield by +9 percentage points (45 → 54 %). Per-candidate compile rate jumped 10× across the full pipeline (4.5 % pre-Stage-3 → 50.6 % post-Stage-4) — the model now produces Foundry-compileable contracts with matching test suites at >50 % per individual candidate.

What this model is good at

Producing self-consistent Foundry-compileable contract + test pairs from a NL spec. Self-oracle test pass rate is 21.4% per candidate, 54% of prompts have ≥1 of 4 passes.
Long-CoT audit reasoning. Stage 2 was trained on ~6k Opus 4.7 audit traces with reasoning steps + structured findings (severity / category / location / impact / fix).
Solidity-idiomatic generation. Stage 0 CPT shifts the base distribution toward modern Solidity patterns (mapping, msg.sender, pragma, custom errors, etc.).

Limitations

Synthetic-data lineage. Stage 1B includes braindao/solidity-base-sft-v2 whose teacher model is undisclosed (likely commodity GPT, not GPT-4-class). Quality ceiling is bounded by the teacher.
Audit-corpus legality. Stage 2 corpus (samscrack/solidity-audit-cot) is Opus-generated under Anthropic API terms over braindao seed contracts. Legal review recommended before any commercial use of the audit-finding outputs.
Held-out eval. This model has never seen samscrack/solidity-eval-2026 (SolBench RACR-4k + differential fuzz) at any stage — that's the gold benchmark.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "samscrack/Qwen3.6-Solidity-27B",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)
tok = AutoTokenizer.from_pretrained("samscrack/Qwen3.6-Solidity-27B")

# Spec → contract + tests
spec = (
    "Implement a Solidity contract that holds a mapping from address to uint256 "
    "balance. Owner can mint to any address. Anyone can transfer their balance to "
    "another address. Include a Foundry test suite covering happy paths and the "
    "owner-only invariant.\n\nProduce both the Solidity contract and a Foundry "
    "test suite that exercises it."
)
msgs = [{"role": "user", "content": spec}]
inputs = tok.apply_chat_template(
    msgs, tokenize=False, add_generation_prompt=True, enable_thinking=True,
)
toks = tok(inputs, return_tensors="pt").to(model.device)
out = model.generate(
    **toks, max_new_tokens=4096, temperature=0.7, top_p=0.9, do_sample=True,
)
print(tok.decode(out[0][toks.input_ids.shape[-1]:], skip_special_tokens=True))

The generated assistant turn has the shape:

<think>...short design rationale...</think>
```solidity
// SPDX-License-Identifier: MIT
pragma solidity ^0.8.x;
contract MyContract { ... }

// test/Contract.t.sol
import "forge-std/Test.sol";
import "../src/Contract.sol";
contract MyContractTest is Test { ... }


## Format envelope

The model was trained on the canonical `<think>...</think>\n```solidity\n{contract}\n```\n\n```solidity\n// test/Contract.t.sol\n{tests}\n``` ` envelope. Most reliable
reproduction is to ask the user prompt to end with: *"Produce both the Solidity
contract and a Foundry test suite that exercises it."*

## Training infrastructure

- 2× NVIDIA RTX PRO 6000 Blackwell Workstation (96 GB each)
- Trainer: TRL 0.22 + Unsloth 2026.4.7 + PyTorch 2.8.0 + cu128
- Inference (sampling for Stage 4 RFT): vLLM 0.19.1 with FP8 dynamic quant +
  FLASH_ATTN backend + Qwen3 reasoning parser

## Citation

@misc{qwen3.6-solidity-27b, author = {Sam Crack (samscrack)}, title = {Qwen 3.6 Solidity (27B): a 5-stage CPT/SFT/RFT recipe for Foundry-compileable Solidity codegen}, year = {2026}, publisher = {HuggingFace}, url = {https://huggingface.co/samscrack/Qwen3.6-Solidity-27B} }


## License

Apache-2.0 (this checkpoint). Underlying training data is from CC-BY/MIT-tier
sources; teacher reasoning content (Stage 2 + Stage 3) was generated under
Anthropic API terms of use as of generation date (2026-05-04). Eval set
`samscrack/solidity-eval-2026` is NOT used at any training stage.

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Model size

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Tensor type

BF16

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