> Canonical page: https://posttrainllm.com/artifacts/qwen3-4b-file-ops-distilled

First specialist package Release-ready weights 2026-06-19

# Qwen3-4B File-Ops Distilled

This is the strongest model win in the repo: a Mac-built specialist reaches 100% on the file-ops hard gate. It is also the clearest example of why routing is mandatory, because breadth drops outside the trained domain.

- agentic
- distillation
- file ops
- BFCL

## Headline Numbers

Evidence class applies to each value: measured = retained evaluation or runtime result; historical = recorded result not freshly reproduced; derived = calculated or decided from records; observed = current artifact state; not-measured = an explicit evidence gap.

File-ops hard gate

 100% up from 58% stock 4B measured

Heldout file-ops

 95% hardgen heldout suite measured

Breadth after tuning

 42.3% down from 59.6% stock measured

Artifact size

 7.5GB local HF/MLX safetensors directory observed

## Competitive Context

**System**

**Metric**

**Score**

**Size / Class**

**Comparable?**

**Readout**

 posttrainllm Qwen3-4B file-ops specialist local file-ops hard gate 100% 4B, 7.5GB package Direct Domain specialist result; not a general BFCL leaderboard submission.

 Stock Qwen3-4B same local file-ops hard gate 58% 4B Direct Before/after delta is +42 points on the frozen domain gate.

 Frontier calibration same local file-ops hard gate ~99-100% frontier API/teacher Direct Used as the ceiling check for whether the eval is a usable ruler.

 [BFCL V4 public leader](https://llm-stats.com/benchmarks/bfcl-v4) overall BFCL V4 accuracy 75.0% large public model Directional LLM Stats snapshot for Qwen3.7 Max; it marks BFCL-V4 rows as self-reported/unverified, so this is market context only.

 [BFCL V4 public average](https://llm-stats.com/benchmarks/bfcl-v4) overall BFCL V4 accuracy 61.1% 13 tracked models Directional LLM Stats reports 13 self-reported rows and 0 verified rows; posttrainllm still needs a full BFCL submission for direct comparison.

 [Qwen3.5-4B public BFCL-V4 row](https://llm-stats.com/benchmarks/bfcl-v4) overall BFCL V4 accuracy 50.3% 4B Directional Closest public 4B-class tool-calling row in the same LLM Stats snapshot, but still not the local file-ops gate.

Direct rows share this artifact's eval setup. Directional rows are useful market context but should not be read as leaderboard claims.

## Measured result

**Gate**

**Stock**

**Specialist**

**Readout**

 File-ops hard gate 0.58 1.00 Domain win

 File-ops hardgen heldout - 0.95 Generalizes within file ops

 Out-of-domain breadth 0.596 0.423 Regression; route only

## Release Blockers

### Breadth regression

The tuned model is wrong to use as a general planner.

 Unblock: Keep all public copy routed-only and include the negative-transfer table.

## Fine-Tune Report Card

The portable before/after proof for this artifact: baseline, candidate, deltas, regressions, slices, performance, eval validity, and the ship/retry/reject decision — with every value labeled measured, derived, historical, skipped, or not recorded. Compiled offline from the recorded factory evidence; no model was run to produce it.

 Shipped — routed only Not fully verified — see the report card for why

[Read the report card →](https://posttrainllm.com/report-cards/qwen3-4b-file-ops-distilled)[Machine-readable JSON](https://posttrainllm.com/report-cards/qwen3-4b-file-ops-distilled.json)

## Evidence

- [Model card](https://github.com/PostTrainLLM/posttrainllm/blob/main/specialists/qwen3-4b-file-ops-distilled/model_card.md)
- [Hugging Face model](https://huggingface.co/posttrainllm/qwen3-4b-file-ops-distilled)
- [Eval report](https://github.com/PostTrainLLM/posttrainllm/blob/main/specialists/qwen3-4b-file-ops-distilled/eval_report.json)
- [Frontier parity writeup](https://posttrainllm.com/docs/learn/tool-calling-frontier-parity/)
- [Specialist registry](https://github.com/PostTrainLLM/posttrainllm/blob/main/specialists/registry.json)

## Next Release Action

Keep routed-only warnings attached and add a consumer pull/load smoke before wiring this into any app.