stancsz/Wrench-4B-Qwen3.6-8E-Experimental-Preview

[!WARNING] Experimental Preview. Not for production use. This model is published for research and preview evaluation only. Do not download it for production deployment or safety-critical workflows. Do not treat benchmark results as production validation. Behavior, APIs, weights, and packaging may change without notice. Use only in an isolated, non-production environment.

Wrench-4B-Qwen3.6-8E Experimental Preview

This Hub repository currently contains the safety-calibrated v7 native-2M candidate, distributed as a 4M-declared portable package. Its stable candidate identity is Wrench-4B-Qwen3.6-8E-Safety-v7-native2M.

Wrench is a pruned, task-specific developer-tool SLM derived from Qwen3.6-35B-A3B. It contains 3,881,244,016 parameters and stays below the 4.25B parameter ceiling.

This is a public experimental artifact. It is downloadable and reproducible, but it is not a claim that the final 4M retrieval-quality or MiniMax-parity gates have passed. The safety candidate is the better bounded-worker checkpoint found so far, but it still requires a complete matched 220-case release evaluation.

Copy the package

hf download stancsz/Wrench-4B-Qwen3.6-8E-Experimental-Preview --local-dir Wrench-4B-Qwen3.6-8E

The canonical distribution format is Hugging Face Safetensors. The package embeds the tokenizer hook, deterministic mechanical lookup runtime, read-only verifier, long-context overlay, hash-bound package metadata, and the FreeToken launcher. It is intended to feel like one model directory, not a separately installed harness.

The embedded worker API is available directly from the downloaded directory:

from wrench_worker import WrenchWorker

worker = WrenchWorker.from_pretrained("./Wrench-4B-Qwen3.6-8E", load_model=False)
result = worker.propose([
    {"role": "user", "content": "Read README.md with a 65536 byte limit."}
])

With load_model=True, ambiguous requests use the standard Transformers model and still pass through the same fail-closed verifier. High-confidence mechanical requests use the embedded route without a model call.

For the standard Hugging Face config and tokenizer path, use Transformers 5.17.0 or newer:

python -m pip install -U "transformers>=5.17.0" huggingface_hub

Run the experimental native endpoint

The bundled launcher requires a compatible FreeToken build and a CUDA GPU:

.\serve_freetoken.ps1

The package declares a 4M input endpoint and uses an 8K recent SWA window in the experimental native profile. The default fast path mechanically reduces noisy payloads to a 64K effective working context. Native direct input and fast staged input are recorded separately in receipts.

The embedded reducer has also passed a 4M mechanical stress diagnostic: 3,999,951 estimated raw tokens reduced to a 92-token model prefill, with 1.0 target-reference recall, 1.0 current-intent preservation, and 1.0 hash-bound reference rate. The measured cold ingest was 98.713 ms and hot selection was 44.441 ms on the local development machine. This is deterministic toolbelt evidence, not an LLM long-context quality or MiniMax-parity claim.

Backend status

• —Hugging Face Safetensors: public experimental package. Transformers 5.17.0+ recognizes the bundled Qwen3.5 MoE architecture and Wrench tokenizer metadata.
• —FreeToken: locally verified experimental backend.
• —The bundled FreeToken path has produced a schema-valid read-only proposal on a small smoke request when given the explicit Wrench output contract.
• —vLLM: requires a registered Wrench architecture adapter.
• —Ollama, llama.cpp, and GGUF: not verified for Wrench hybrid attention and lookup semantics. Do not assume a generic GGUF conversion preserves these features.

Known limits

The base checkpoint config is 2M position-capable. The 4M probe uses a runtime RoPE extension and is not long-context training. Native 4M retrieval quality, throughput under production concurrency, and the full MiniMax matched-workflow acceptance suite remain open measurements.

The standard Transformers path has verified architecture/configuration, tokenization, and full weight loading. Generation quality through plain Transformers is not yet a pass and is intentionally not claimed here. Use the bundled FreeToken profile for the current experimental generation path.

Wrench has no direct mutation authority. It proposes bounded developer-tool actions or abstains; a surrounding verifier must enforce execution policy.