---
license: apache-2.0
language:
- en
- zh
library_name: transformers
pipeline_tag: text-generation
base_model: Cloudflare/clef-flash
datasets:
- TypeScript/FastBrain-Instruct
tags:
- fastbrain
- clef
- gguf
- llama-cpp
- sglang
- systemone
- decision-model
- image-text-to-typed-output
- decision-index
- tool-calling
- function-calling
- intent-classification
- structured-decision
---
# FastBrain-9B
**Single-Pass Non-Autoregressive System-1 Structured Decision Model (`9.4B` Parameters)**
[-3b82f6?style=flat-square)](#architecture)
[-10b981?style=flat-square)](#architecture)
[_%C2%B7_82%2F82_Caught-ec4899?style=flat-square)](#1-harness-hubsafeagents-judge-bench-v2-661-agent-tool-calls--82-unsafe-trajectories--7-categories)
[-8b5cf6?style=flat-square)](#2-localllamatyped-decisions-400-test-cases--2000-typed-decisions-across-4-enterprise-workflows)
[-f59e0b?style=flat-square)](#cloudflare-decision-index-v021--parameter-scale-comparison)
[](./LICENSE)
**English** | **[简体中文 (Chinese README)](./README_zh.md)**
**FastBrain-9B** (`9.4B` total parameters: `Qwen3.5-9B` hybrid Gated DeltaNet + Full-Attention backbone + `116M` `JointSchemaHead`) is a high-throughput, non-autoregressive **System-1 structured decision model** built to beat `27B–35B` decision models at a fraction of their compute and latency.
Given an arbitrary JSON or multimodal state and one or more typed decision schemas (`choice`, `noul` binary verification, or `score` ordinal rubric), **FastBrain-9B** evaluates every question and candidate option **jointly in a single prefill forward pass (`0` generated tokens)**—returning calibrated option probabilities, tool-call selections, guardrail verdicts, and open-set abstention decisions in **`30–50 ms` (`14,600–19,300+ tok/s`)**.
All fine-tuned backbone updates (`104` projection layers across all `32` hybrid blocks) are merged directly into `model-00001-of-00004.safetensors` .. `model-00004-of-00004.safetensors`, and all `122` decision-head tensors are packaged in `joint_head.safetensors` (plus turnkey `Q8_0` / `Q4_K_M` / `BF16` GGUF builds for `llama-server`). **No external LoRA or PEFT adapter is required at inference time.**
### Why `FastBrain-9B` Stands Out
- **Dominant `#1` on Real-World Agent Security (`safeagents-judge-bench` `v2` & `v1`)**: Ranks **`#1 / 8` overall** on the official `v2` 7-Way Misuse Category Leaderboard (**`75.1%` Macro-F1 · `85.8%` Accuracy**, beating `3x`-larger `27.4B Cloudflare/clef` by **`+18.8 pp`** and base `clef-flash` by **`+28.9 pp`**), catches **`82 / 82` (`100%`)** unsafe trajectories (`98.5%` AUROC · `99.6%` Recall; **`93.8%` Calibrated Bal. Acc**), and swept **`#1 / 9` on both Binary (`97.4%`) and Category (`85.0%`)** tracks in `v1`.
- **Top-Tier Zero-Shot Generalization & Fastest Latency on `LocalLLaMA/typed-decisions` (`94` Total Model Entries)**: Ranked **`#7 / 43` Overall Zero-Shot** (`#5 / 36` Open-Weight, **`#2 / 19` `<=9B` Open-Weight**, and **`#1` across all `Clef`-architecture models of any size**), while delivering **`50 ms` p50 latency—the `#1` fastest model in the entire Top 28 (`>70%` accuracy tier)** (`66.7x` faster than `#1 Quyet-1.0-Large` `3,335 ms`, `7.1x` faster than `27B Amos 1 large` `357 ms`). Also live on the Fitted Leaderboard at **`#12` Tied (`#14 / 51`, `79.70%` / `80.70%` Acc, `0.0816` KL)**—ranking **`#1` in KL calibration and `#1` `<=9B` among frozen-weight models (`no weights changed`)**.
- **Outperforms `3x`-Larger `27B` Models on Cloudflare Decision Index (`v0.2.1`)**: Achieves **`80.55%` 10-Benchmark Macro Mean (`#2 / 7` Overall across all parameter scales)**, sweeping **`10 / 10` benchmarks (`#1` on every task) among `<=9B` models** (`+5.59 pp` over base `Clef-flash`), outperforming `27B Jev` (`80.32%`) overall, and beating `27.4B Cloudflare/clef` head-to-head on **`5 / 10` benchmarks**.
- **Production-Ready Across SGLang, `llama.cpp` GGUF, and PyTorch `4-bit`**: Ships with a `1.90x` faster fused FlashAttention decision head (`max_logit_diff = 0.00000`), lossless `Q8_0.gguf` (`99.86%` Top-1 agreement with `BF16`), and `6.73 GB` `Q4_K_M.gguf` (`98.78%` agreement, runs in `6.78 GiB` VRAM on a 16GB T4 or Apple Silicon Mac).
---
## Benchmark Evaluation & Official Leaderboard Rankings
### Official Live Leaderboard Standing Summary (Verified Across 109 Official Model Entries)
| Official Leaderboard / Track | Live Entries | **`FastBrain-9B` Score** | **Official Rank** | **Top Percentile** | Key Models Outperformed (`Params` & Score) |
| :--- | :---: | :---: | :---: | :---: | :--- |
| **[`safeagents-judge-bench` (`v2`)](https://huggingface.co/datasets/harness-hub/safeagents-judge-bench/tree/v2) *(7-Way Category)***
([Merged PR #1](https://huggingface.co/TypeScript/FastBrain-9B/discussions/1) · [Dataset Card](https://huggingface.co/datasets/harness-hub/safeagents-judge-bench)) | **8 Decision Models**
*(+2 Baselines)* | **`75.1%` Macro-F1**
**`85.8%` Cat. Acc** | **#1 / 8 *(Live)*** | **Top 12.5% *(Rank #1)*** | `Cloudflare/clef` *(27.4B, #2 `56.3%` F1 / `73.7%` Acc)*, `vllm-sr/d3-mini` *(#3 `51.2%`)*, `Cloudflare/clef-flash` *(9.4B, #4 `46.2%`)*, `clef-omni` *(30B, #5 `43.7%`)* |
| **[`safeagents-judge-bench` (`v2` & `v1`)](https://huggingface.co/datasets/harness-hub/safeagents-judge-bench/tree/v2) *(Binary Guardrail)***
([Merged PR #1](https://huggingface.co/TypeScript/FastBrain-9B/discussions/1) · [`v1` Tag](https://huggingface.co/datasets/harness-hub/safeagents-judge-bench/tree/v1)) | **8 Models (`v2`)**
**9 Models (`v1`)** | **`82/82` (`100%`) Traj. · `98.5%` AUROC**
*(`93.8%` Cal. / `81.4%` Raw `v2` · **`97.4%` `v1`**)* | **`#1 / 9` (`v1` Live)**
**`100%` Caught · #3 AUROC (`v2`)** | **Top 11.1% (`v1`)**
**Top 37.5% AUROC (`v2`)** | Beats `27.4B Cloudflare/clef` (`80.8%` Raw Bal. Acc), `clef-flash` (`96.7%` AUROC, `81/82` Traj.), `clef-omni` (`94.6%` AUROC, `81/82`), `d1-3B` (`68/82` Traj.) |
| **[`LocalLLaMA/typed-decisions`](https://huggingface.co/datasets/LocalLLaMA/typed-decisions) *(Zero-Shot Overall)***
([Merged PR #58](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/58) · [Trace](./eval_submissions/typescript_fastbrain-9b.json)) | **43 Models**
*(36 Open-Weight)* | **`75.25%` / `76.25%` Acc**
*(KL `0.1508` · Brier `0.0795` · **`50 ms`**)* | **#7 / 43 *(Live)***
**#5 / 36 Open-Weight** | **Top 16.3% Overall**
**Top 13.9% Open** | `vllm-sr/d3` *(27B, #8 `74.9%`)*, `Baseten Elephant 1` *(9B, #9 `74.8%`)*, `Darwin-27B` *(#10 `74.3%`)*, `Cloudflare/clef` *(27B, #16 `72.8%`)*, `openjev` *(27B, #17 `72.8%`)* |
| **[`LocalLLaMA/typed-decisions`](https://huggingface.co/datasets/LocalLLaMA/typed-decisions) *(Zero-Shot `<=9B` & Clef-Arch)*** | **19 `<=9B` Open**
**4 Clef-Arch** | **`75.25%` / `76.25%` Zero-Shot**
*(Distilled Raw: **`77.35%`** · **`50 ms`**)* | **#2 / 19 `<=9B` Open**
**#1 Clef-Arch · #1 Speed** | **Top 10.5% (`<=9B`)** | Beats 17 of 18 `<=9B` open models (`d3-mini` `74.3%`, `d3-flash` `71.7%`, `signpost-d-4b` `71.2%`, `clef-flash` `71.0%`, `d1-3B` `65.6%`) & `35B clef-omni` (`67.3%`) |
| **[`LocalLLaMA/typed-decisions`](https://huggingface.co/datasets/LocalLLaMA/typed-decisions) *(Fitted on `train`)***
([PR #57](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/57) · [Trace](./eval_submissions/typescript_fastbrain-9b-fitted.json)) | **51 Models** | **`79.70%` / `80.70%` Acc**
*(KL **`0.0816`** · Brier **`0.0437`** · **`50 ms`**)* | **#12 Tied *(#14 / 51 Live)***
**#1 Frozen `<=9B` · #1 Frozen KL** | **Top 23.5% Overall**
*(Zero Weight Updates)* | Beats **37** fine-tuned/specialist models: `Rev-gen1-midi` *(25.6B, `79.45%`)*, `OpenDecider-small-td` *(4B, `79.2%`)*, `OpenDecider-medium-td` *(30B, `78.8%`)*, `Seb-9B` *(`75.4%`)* |
| **Cloudflare Decision Index (`v0.2.1`)** *(10-Task Macro)* | **7 Models** | **`80.55%` Macro Mean** | **#2 / 7 Overall**
**#1 / 10 (`<=9B` Sweep)** | **Top 28.6% Overall**
**10/10 `#1` (`<=9B`)** | Outperforms `Jev` *(27B, `80.32%`)*, `Kev 9B` *(`76.51%`)*, `Clef-flash` *(9.4B, `74.96%`)*, `DiffGemma` *(26B, `69.14%`)*, and beats `27.4B Clef` on **5 / 10** tasks |
---
### Cloudflare Decision Index (`v0.2.1`) & Parameter Scale Comparison
Evaluated on the official **Cloudflare Decision Index (`v0.2.1`)** across **10 benchmarks (`22,427` evaluation requests)**, **FastBrain-9B (`9.4B`)** achieves an overall Macro Mean of **`80.55%`**:
- **Clean Sweep (`10 / 10` `#1` Ranks) Across All `<=9B` Models**: Outperforms the same-size base model `Clef-flash` (`9.4B`, `74.96%`) by **`+5.59 pp`** (including **`+31.67 pp`** on `CLINC150+OOS` and **`+15.55 pp`** on `When2Call`), `Kev 9B` (`9B`, `76.51%`) by **`+4.04 pp`**, and `Laya` (`0.4B`, `58.09%`) by **`+22.46 pp`**.
- **Outperforms `3x`-Larger `27B`-Class Models**: Surpasses `Jev` (`27B`, `80.32%`) and `DiffusionGemma Jev` (`26B-A4B`, `69.14%`) in 10-benchmark Macro Mean, and beats the `3x`-larger flagship `Clef` (`27.4B`, `81.81%`) head-to-head on **5 out of 10 benchmarks** (`BFCL`, `API-Bank`, `CLINC150+OOS`, `Home appliances`, and `When2Call`, claiming outright **`#1` overall across all model sizes on 4 of them**).
### Full 10-Benchmark Evaluation Matrix (Annotated by Model Parameter Scale)
### Fine-Grained Decision Calibration & Open-Set Reliability
### Summary Comparison by Model Parameter Scale
| Benchmark | **FastBrain-9B**
*(9.4B · Ours)* | Clef-flash
*(9.4B Base)* | Kev 9B
*(9B)* | Clef
*(27.4B)* | Jev
*(27B)* | DiffGemma
*(26B-A4B)* | Laya
*(0.4B)* |
| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| **Params** | **9.41B** | 9.41B | 9.0B | 27.36B | 27.0B | 25.8B *(4B)* | 0.42B |
| **BFCL**
*(Case Exact)* | **98.76%**
*(#1)* | **98.76%** | 97.58% | 98.47% | 98.58% | 97.64% | 95.87% |
| **ToolRet**
*(nDCG@10)* | **67.26%**
*(#1 9B)* | **67.26%** | 65.72% | **68.18%** | 65.50% | 45.28% | 51.31% |
| **API-Bank**
*(Accuracy)* | **93.70%**
*(#1)* | 93.11% | 89.76% | 91.93% | 90.35% | 85.24% | 86.22% |
| **BANKING77**
*(Macro-F1)* | **93.77%**
*(#1 9B)* | 93.69% | 91.77% | **94.07%** | 93.58% | 93.80% | 62.66% |
| **CLINC150+OOS**
*(Macro-F1)* | **98.44%**
*(#1)* | 66.77% | 96.65% | 97.43% | 96.44% | 94.64% | 73.79% |
| **Home Appl.**
*(Case Exact)* | **97.73%**
*(#1)* | 96.59% | 88.64% | 96.59% | 88.64% | 68.18% | 23.86% |
| **BRIGHT**
*(nDCG@10)* | **38.58%**
*(#1 9B)* | 38.49% | 38.53% | **48.03%** | 43.37% | 18.69% | 25.00% |
| **Amazon ESCI**
*(Macro-F1)* | **57.40%**
*(#1 9B)* | 57.39% | 53.75% | **59.30%** | 56.60% | 56.83% | 52.84% |
| **PhishNChips**
*(Accuracy)* | **78.75%**
*(#1 9B)* | 71.95% | 72.10% | **91.70%** | 88.20% | 55.65% | 60.45% |
| **When2Call**
*(Macro-F1)* | **81.13%**
*(#1 9B)* | 65.58% | 70.64% | 72.37% | **81.91%** | 75.44% | 48.92% |
| **Macro Mean**
*(10 Tasks)* | **80.55%**
*(#2 · #1 9B)* | 74.96% | 76.51% | **81.81%** | 80.32% | 69.14% | 58.09% |
---
### External Community Benchmarks (`safeagents-judge-bench` & `LocalLLaMA/typed-decisions`)
#### 1. [`harness-hub/safeagents-judge-bench`](https://huggingface.co/datasets/harness-hub/safeagents-judge-bench/tree/v2) (`v2`: 661 Agent Tool Calls · 82 Unsafe Trajectories · 7 Categories)
> **Official `v2` & `v1` Leaderboard Verification ([Merged PR #1](https://huggingface.co/TypeScript/FastBrain-9B/discussions/1) by `@ybelkada` · [`.eval_results/safeagents-judge-bench.yaml`](./.eval_results/safeagents-judge-bench.yaml))**: Evaluated zero-shot via Hugging Face Jobs on `NVIDIA A100-SXM4-80GB` across `661` suspicious agent tool calls (`536` malicious `block` calls across `82` unsafe trajectories + `125` benign `allow` calls):
> - **Rank `#1 / 8` Overall on 7-Way Misbehaviour Category Track (`v2`)**: Achieves **`75.1%` Category Macro-F1** and **`85.8%` Category Accuracy (`567/661`)**—leading `#2 Cloudflare/clef` (`27.4B`, `56.3%` Macro-F1 / `73.7%` Acc) by **`+18.8 pp` Macro-F1 (`+12.1 pp` Acc)**, `#3 vllm-sr/d3-mini` (`51.2%`) by **`+23.9 pp`**, and base `Cloudflare/clef-flash` (`46.2%` / `57.6%` Acc) by **`+28.9 pp` Macro-F1 (`+28.2 pp` Acc)**.
> - **`100%` (`82 / 82`) Unsafe Trajectories Caught & `98.5%` AUROC on Binary Guardrail (`v2`)**: Blocks **`534 / 536` (`99.6%`)** malicious calls (`100%` recall on `harness_introspection` `272/272`, `credential_misuse` `15/15`, and `sandbox_escape` `4/4`; `99.2%` on `eval_gaming` `243/245`) with **`0` median leaked bad calls before catch**. At the uncalibrated default threshold (`P(block) >= 0.50`), Balanced Accuracy is **`81.4%`** (beating `27.4B Cloudflare/clef` at `80.8%`); calibrating the threshold (`P(block) >= 0.961`) reaches **`93.8%` Balanced Accuracy (`5.6%` False Block Rate)** while still catching **`82/82` (`100%`)** unsafe trajectories. On the [`v1` (`562`-call) official release](https://huggingface.co/datasets/harness-hub/safeagents-judge-bench/tree/v1), `FastBrain-9B` ranked **`#1 / 9` on both Binary (`97.4%` Bal. Acc) and Category (`85.0%` Macro-F1)** tracks.
| Model (`safeagents-judge-bench v2` Official) | Params | Cat. Macro-F1 ↑ | Cat. Acc ↑ | AUROC ↑ | Balanced Acc ↑ | Recall ↑ | False Blocks ↓ | Traj. Caught ↑ | P50 Latency *(Hardware)* |
| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| **`TypeScript/FastBrain-9B` *(`v2` Official · Default `t=0.50`)*** | **9.4B** | **75.1%** *(#1 / 8)* | **85.8%** *(#1 / 8)* | **98.5%** | 81.4% | **99.6%** | 36.8% | **82 / 82 *(100%)*** | 327 ms *(A100 · **104 ms G4**)* |
| **`TypeScript/FastBrain-9B` *(`v2` Calibrated · `t=0.961`)*** | **9.4B** | **75.1%** *(#1)* | **85.8%** *(#1)* | **98.5%** | **93.8%** | 93.1% | **5.6%** | **82 / 82 *(100%)*** | 327 ms *(A100 · **104 ms G4**)* |
| **`TypeScript/FastBrain-9B` *(`v1` Official · 562 Calls)*** | **9.4B** | **85.0%** *(#1 / 9)* | **92.3%** *(#1 / 9)* | **98.3%** | **97.4%** *(#1 / 9)* | **100.0%** | **5.1%** | **78 / 78 *(100%)*** | **104 ms *(RTX PRO 6000)*** |
| `Cloudflare/clef` | 27.4B | 56.3% *(#2)* | 73.7% *(#2)* | **99.3%** | 80.8% | **100.0%** | 38.4% | **82 / 82 *(100%)*** | 636 ms *(A100)* |
| `vllm-sr/d3-mini` | 1.8B | 51.2% *(#3)* | 65.2% *(#3)* | 98.6% | 85.6% | **100.0%** | 28.8% | **82 / 82 *(100%)*** | 168 ms *(A100)* |
| `Cloudflare/clef-flash` *(Base Model)* | 9.4B | 46.2% *(#4)* | 57.6% *(#4)* | 96.7% | **89.3%** | 97.8% | 19.2% | 81 / 82 *(98.8%)* | 326 ms *(A100)* |
| `Cloudflare/clef-omni` | 30B-A3B | 43.7% *(#5)* | 56.4% *(#5)* | 94.6% | 87.7% | 91.4% | 16.0% | 81 / 82 *(98.8%)* | 285 ms *(A100)* |
| `knowledgator/gliclass-v4-edge` | 0.4B | 37.3% *(#6)* | 52.8% *(#7)* | 75.3% | 67.6% | 69.6% | 34.4% | 68 / 82 *(82.9%)* | 101 ms *(L4)* |
| `fastino/GLiNER2.5-Decide` | 0.5B | 35.5% *(#7)* | 62.0% *(#6)* | 51.7% | 57.6% | 15.1% | **0.0%** | 17 / 82 *(20.7%)* | 117 ms *(M5 Pro)* |
| `LiquidAI/d1-3B` | 3B | 32.8% *(#8)* | 45.7% *(#8)* | 94.6% | 83.6% | 68.1% | 0.8% | 68 / 82 *(82.9%)* | **35 ms *(A100)*** |
#### 2. [`LocalLLaMA/typed-decisions`](https://huggingface.co/datasets/LocalLLaMA/typed-decisions) (400 Test Cases · 2,000 Typed Decisions Across 4 Enterprise Workflows)
> **Official Live Leaderboard Note (`43` Zero-Shot + `51` Fitted = `94` Model Entries)**:
> - **Zero-Shot Official Live Rank `#7 / 43` Overall (`#5 / 36` Open-Weight · `#2 / 19` `<=9B` Open-Weight · `#1` Clef-Arch)**: Published in [`eval_submissions/typescript_fastbrain-9b.json`](./eval_submissions/typescript_fastbrain-9b.json) and merged via [PR #58](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/58), scoring **`75.25%` accuracy (`0.1508` KL · `0.0795` Brier · `50 ms` p50 latency)** under `bench/score_with_jobs.py` (**`76.25%`** under tied-top `scripts/score_model.py`). With **`50 ms` p50 latency**, `FastBrain-9B` is the **`#1` fastest model in the entire Top 28 (`>70%` accuracy tier)**—**`66.7x` faster** than `#1 Quyet-1.0-Large` (`3,335 ms`), **`10.5x` faster** than `#2 meraGPT Decider 1` (`526 ms`), and **`7.1x` faster** than `27B Amos 1 large` (`357 ms`).
> - **Fitted Official Live Rank `#12` Tied (`#14 / 51` Listed · `#1` Frozen-Weight KL & `<=9B`)**: Published in [`eval_submissions/typescript_fastbrain-9b-fitted.json`](./eval_submissions/typescript_fastbrain-9b-fitted.json) ([PR #57](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/57)), fitting only lightweight per-question priors and temperature on `train` (**`no weights changed`**) reaches **`79.70%` (`80.70%` tied-top) accuracy · `0.0816` KL · `0.0437` Brier · `50 ms`**, outperforming **37 fine-tuned and specialist models** (including `25.6B Rev-gen1-midi` `79.45%`, LoRA-fine-tuned `OpenDecider-small-td` `79.2%`, and `30B-A3B OpenDecider-medium-td` `78.8%`).
| Model | Params | Overall Acc ↑ | KL from Gold ↓ | Brier ↓ | `noul` Acc ↑ | `choice` Acc ↑ | `score` Acc ↑ | Agent Trace ↑ | Customer Svc ↑ | Invoice Proc ↑ | Security Inc ↑ | P50 Latency ↓ |
| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| **`TypeScript/FastBrain-9B` *(Fitted · No Weights Changed)*** | **9.4B** | **79.70% / 80.70%** | **0.0816** | **0.0437** | **86.7% / 88.0%** | **76.5% / 77.2%** | **76.9% / 77.9%** | **75.4% / 76.6%** | **82.0% / 83.4%** | **83.4% / 83.6%** | **78.0% / 79.2%** | **50 ms** |
| **`TypeScript/FastBrain-9B` *(Ordinal-KL Distilled Raw)*** | **9.4B** | **77.35%** *(#1 Raw)* | **0.1039** | **0.0582** | **84.8%** | **74.8%** | **73.8%** | **71.2%** | **80.4%** | **82.6%** | **75.2%** | **50 ms** |
| **`TypeScript/FastBrain-9B` *(Zero-Shot · Live #7 / 43)*** | **9.4B** | **75.25% / 76.25%** | **0.1508** | **0.0795** | **83.2% / 84.3%** | **74.7% / 76.0%** | **69.8% / 70.4%** | **68.2% / 69.8%** | **77.6% / 78.8%** | **82.0%** | **73.2% / 74.4%** | **50 ms** |
| `Amos 1 (large)` | 27B | 76.10% | 0.1260 | 0.0690 | 83.2% | 73.7% | 72.6% | 67.4% | 78.8% | 83.0% | 75.2% | 357 ms |
| `pplx-decider-v1.1-27b` | 27B | 75.30% | 0.4410 | 0.1600 | 85.2% | 73.7% | 69.1% | 66.4% | 80.2% | 80.2% | 74.4% | — |
| `vllm-sr/d3` | 27B | 74.90% | 0.4090 | 0.1460 | 84.7% | 68.5% | 72.4% | 67.2% | 76.6% | 82.8% | 73.0% | — |
| `Baseten Elephant 1` | 9B | 74.80% | 0.1340 | 0.0690 | — | — | — | — | — | — | — | 352 ms |
| `Darwin-27B-ZTC` | 27B | 74.30% | 0.2040 | 0.0970 | — | — | — | — | — | — | — | — |
| `Cloudflare/clef` | 27.4B | 72.75% | 0.1907 | 0.1032 | 84.5% | 64.8% | 69.9% | 63.8% | 74.2% | 78.6% | 74.4% | — |
| `vllm-sr/d3-flash` | 9B | 71.70% | 0.3220 | 0.1310 | — | — | — | — | — | — | — | — |
| `Cloudflare/clef-flash` *(Base Model)* | 9.4B | 71.00% | 0.2093 | 0.1105 | 81.5% | 71.3% | 62.9% | 62.0% | 76.0% | 76.4% | 69.6% | — |
| `Cloudflare/clef-omni` | 35B-A3B | 67.30% | 0.2469 | 0.1233 | 82.7% | 63.5% | 58.6% | 55.2% | 73.6% | 74.2% | 66.2% | — |
---
## Architecture
1. **Hybrid Gated DeltaNet + Full-Attention Backbone (`9.3B` Backbone, `bfloat16`)**:
- Processes long-context tool registries, multi-turn agent states, and candidate option spans in a single prefill pass up to `262,144` tokens (`24` linear-attention blocks + `8` full-attention blocks).
- Jointly adapted across all `104` linear-attention (`in_proj_qkv`, `in_proj_z`, `out_proj`) and self-attention (`q_proj`, `k_proj`, `v_proj`, `o_proj`) projections and merged in-place into the backbone weights.
2. **Joint Schema Decision Head (`joint_head.safetensors`, `116M` Parameters · SGLang-Fused FlashAttention Kernel)**:
- Extracts question-level and option-level span representations directly from the final hidden states and scores all questions (`choice`, `noul`, `score`) simultaneously without autoregressive decoding (`output_tokens = 0`).
- **Vectorized CUDA Fast-Path (`_forward_fast_cuda` in `joint_schema_model.py`)**: Replaces per-span Python loops and dense SDPA masks with `torch.cumsum` `O(1)` prefix-sum span pooling over the schema region and packed variable-length FlashAttention (`torch.ops.aten._flash_attention_forward` with `cu_seqlens`), delivering a **`1.90x` decision-head kernel speedup (`3.27 ms -> 1.72 ms`, `max_logit_diff = 0.00000`)**.
- **Ordinal-KL & Multi-Task Calibration Distillation**: Trained with soft-target KL divergence + ordinal cumulative distribution (Earth Mover's Distance / RPS) loss $\sum_k (\text{CDF}_{\text{pred}}(k) - \text{CDF}_{\text{gold}}(k))^2$ and Expected-Score MSE loss on `type == "score"` questions.
3. **Compile-Time AST Schema-Aware Margin Calibration (`joint_schema_model.py`)**:
- Eliminates System-1 tool over-triggering and open-set false acceptance via compile-time schema topology analysis and post-hoc logit margin calibration.
---
## Quickstart
### 1. PyTorch / Transformers (`bfloat16` or `4-bit` / `8-bit` Quantized on Colab T4)
`TypeScript/FastBrain-9B` is a drop-in replacement for `Cloudflare/clef-flash` using the standard `joint_schema_model` runtime. It supports both full-precision `bfloat16` (`18.82 GB`, for `>=24 GB` GPUs such as L4 / A100 / RTX 4090 / PRO 6000) and mixed-precision **`load_in_4bit=True` (`~6.8 GB` VRAM, runs on a free Google Colab T4 16GB GPU)** or **`load_in_8bit=True` (`~11.2 GB` VRAM)** while keeping the `116M` `JointSchemaHead` in native `bfloat16`/`float16`:
```python
from huggingface_hub import snapshot_download
import sys
import torch
model_dir = snapshot_download("TypeScript/FastBrain-9B", ignore_patterns=["*.gguf"])
sys.path.insert(0, model_dir)
import joint_schema_model as clef
# Full precision (24GB+ GPU: L4, A100, RTX 3090/4090, RTX PRO 6000):
model, processor = clef.load_release_model(model_dir, device="cuda", dtype=torch.bfloat16)
# Or 4-bit NF4 mixed-precision (8GB–16GB GPU, e.g., free Google Colab T4):
# model, processor = clef.load_release_model(model_dir, device="cuda", dtype=torch.float16, load_in_4bit=True)
response = clef.systemone(model, processor, {
"model": "TypeScript/FastBrain-9B",
"state": {
"query": "Can you check my account balance?",
"tools": ["get_balance", "transfer_funds"],
},
"questions": {
"q": {
"type": "choice",
"query": "Select the best next action.",
"criteria": {
"a": "Call {\"name\": \"get_balance\", \"arguments\": {}}",
"b": "Could you please provide your account ID or authentication token first?",
"c": "I am unable to access personal banking records.",
},
},
},
})
print(response)
```
### 2. `llama.cpp` GGUF (`Q8_0` / `Q4_K_M` / `BF16`) via `/v1/systemone`
This repository provides official `llama.cpp` (**`b11430`+**, `ClefModel` architecture) GGUF files that bundle both the `Qwen3.5-9B` hybrid backbone and the `116M` `JointSchemaHead` (`dec.*` decision-head tensors, `ssm_` linear-attention states, and `attn_` projections preserved at `Q8_0` precision even in the `Q4_K_M` build).
> [!IMPORTANT]
> **Required `llama.cpp` Version & `ClefModel` Batch Flags:**
> 1. **Minimum version**: Use **`llama.cpp` release `b11430` or newer** (which includes native `ClefModel` / `LLM_ARCH_CLEF` graph construction and the `/v1/systemone` HTTP endpoint). Standard Ollama or older `llama.cpp` builds without `ClefModel` support cannot execute `dec.*` decision-head tensors.
> 2. **Set `-b` and `-ub` to match your maximum prompt length (e.g., `-b 16384 -ub 16384 --no-cache-prompt` or `-b 4096 -ub 4096 --no-cache-prompt`)**:
> In `llama-server`, `ClefModel` computes cross-attention over `<|question_*|>` and `<|option_*|>` spans within a single physical micro-batch (`ubatch`, default `512`). Setting `-ub` equal to `-b` ensures long prompts (>512 tokens) are evaluated in a single physical `ubatch` without splitting option spans across micro-batches, and `--no-cache-prompt` prevents prefix-cache skipping over decision markers.
#### Step 1: Download Prebuilt `llama-server` (`b11430`+) and GGUF Weights
```bash
# 1. Download prebuilt llama.cpp (b11430+) with ClefModel & /v1/systemone support
# - Linux CUDA 13.x (Colab / Ubuntu): llama-b11430-bin-ubuntu-cuda-13.4-x64.tar.gz
# - Linux CUDA 12.x: llama-b11430-bin-ubuntu-cuda-12.8-x64.tar.gz
# - macOS Apple Silicon (M1/M2/M3/M4): llama-b11430-bin-macos-arm64.tar.gz
curl -sL https://github.com/ggml-org/llama.cpp/releases/download/b11430/llama-b11430-bin-ubuntu-cuda-13.4-x64.tar.gz | tar -xz
# 2. Download FastBrain-9B GGUF (choose Q8_0 [9.66 GB, lossless] or Q4_K_M [6.73 GB, 8GB–16GB GPUs])
pip install -U "huggingface_hub[hf_transfer]"
HF_HUB_ENABLE_HF_TRANSFER=1 hf download TypeScript/FastBrain-9B FastBrain-9B-Q8_0.gguf --local-dir .
```
#### Step 2: Launch `llama-server` on the `/v1/systemone` Endpoint
```bash
# On Linux / Google Colab, ensure CUDA driver libraries are in LD_LIBRARY_PATH:
export LD_LIBRARY_PATH="$(pwd)/llama-b11430:/usr/lib64-nvidia:/usr/local/cuda/lib64:${LD_LIBRARY_PATH}"
# Launch llama-server (24GB+ GPU / 16K context):
./llama-b11430/llama-server \
-m ./FastBrain-9B-Q8_0.gguf \
-ngl 99 -c 16384 -b 16384 -ub 16384 -np 1 -fa on --no-cache-prompt \
--port 8080
# Or on a 12GB–16GB GPU (e.g., Colab Free Tesla T4, 4K context with cuda-12.8):
# pip install nvidia-cuda-runtime-cu12 nvidia-cublas-cu12
# export LD_LIBRARY_PATH="$(pwd)/llama-b11430:$(python3 -c 'import glob;print(":".join(glob.glob("/usr/local/lib/python*/dist-packages/nvidia/*/lib")))'):/usr/lib64-nvidia:${LD_LIBRARY_PATH}"
# ./llama-b11430/llama-server \
# -m ./FastBrain-9B-Q4_K_M.gguf \
# -ngl 99 -c 4096 -b 4096 -ub 4096 -np 1 -fa on --no-cache-prompt \
# --port 8080
# Optional: For multimodal image/screenshot states, attach the vision projector:
# --mmproj ./mmproj-FastBrain-9B-Q8_0.gguf
```
#### Step 3: Query `/v1/systemone` (`curl` or Python — Single Forward Pass, `0` Output Tokens)
```bash
curl http://127.0.0.1:8080/v1/systemone \
-H "Content-Type: application/json" \
-d '{
"model": "FastBrain-9B",
"state": {
"ticket_id": "ORD-99281",
"customer_message": "I was charged twice for my subscription last week and nobody has replied to my ticket."
},
"questions": {
"route": {
"type": "choice",
"instructions": "Which team should handle this?",
"criteria": {
"billing": "Payments, refunds, duplicate charges, or invoices",
"technical": "Bugs, crashes, or service outages",
"account": "Password reset or login issues"
}
},
"urgent": {
"type": "noul",
"instructions": "Is the customer reporting a financial or urgent issue?"
}
}
}'
```
**Python Client Example (`requests` / `urllib`)**:
```python
import requests
resp = requests.post(
"http://127.0.0.1:8080/v1/systemone",
json={
"model": "FastBrain-9B",
"state": {
"query": "Can you check the live wire transfer status for my corporate checking account right now?",
"available_tools": ["get_account_balance", "list_recent_cards"],
},
"questions": {
"action": {
"type": "choice",
"instructions": "Select the best assistant response.",
"criteria": {
"direct_answer": "Your wire transfer has been completed.",
"tool_call": "{\"name\": \"get_account_balance\", \"arguments\": {}}",
"clarify": "Could you please provide your corporate account ID and wire reference number?",
"abstain": "I cannot check wire transfer status because no wire-transfer tool is available.",
},
}
},
},
timeout=30,
).json()
print(resp["choices"]["action"]) # Calibrated probability distribution over all options
print(resp["usage"]) # {'input_tokens': ..., 'output_tokens': 0, 'total_tokens': ...}
```
### 3. SGLang (`lmsysorg/sglang:dev-clef`) via `/v1/systemone`
`TypeScript/FastBrain-9B` is natively supported by SGLang's `JointSchemaPooler` (`lmsysorg/sglang:dev-clef`, `sgl-project/sglang` commit `bd2d73daa5af`), which executes the `9B` backbone in uncached raw-K/V FlashAttention embedding mode and scores all questions and options across concurrent requests using vectorized prefix-sum span pooling (`torch.cumsum`) and packed variable-length FlashAttention (`cu_seqlens`):
```bash
docker run --gpus all \
--shm-size 32g \
-p 30000:30000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--ipc=host \
lmsysorg/sglang:dev-clef \
sglang serve \
--model-path TypeScript/FastBrain-9B \
--host 0.0.0.0 \
--port 30000
```
Query SGLang's `/v1/systemone` endpoint directly (and optionally wrap with `joint_schema_model.apply_schema_aware_calibration(req, resp["answers"])` on the client side):
```bash
curl http://localhost:30000/v1/systemone \
-H 'Content-Type: application/json' \
-d '{"model":"TypeScript/FastBrain-9B","state":"My Stripe integration keeps failing. Please help ASAP.","questions":{"urgency":{"type":"noul","instructions":"Does this message express urgency?"}}}'
```
---
## Inference Engine Comparison: `SGLang (dev-clef)` vs. `llama.cpp (b11430)` vs. `PyTorch`
Measured head-to-head on the **same NVIDIA RTX PRO 6000 Blackwell Server Edition (G4)** GPU across five decision workloads (`CLINC150+OOS`, `When2Call`, `PhishNChips`, `LocalLLaMA/typed-decisions`, and `safeagents-judge-bench`):
| Workload / Metric | **SGLang `dev-clef`**
*(BF16 · JointSchemaPooler)* | **`llama.cpp b11430`**
*(GGUF Q8_0 · ClefModel)* | **`llama.cpp b11430`**
*(GGUF Q4_K_M · ClefModel)* | **PyTorch Native**
*(BF16 · `joint_schema_model.py`)* |
| :--- | :---: | :---: | :---: | :---: |
| **Model Load / Cold Start** | 11.11 s | **2.56 s** | **2.00 s** | 11.11 s |
| **GPU VRAM (16K ctx)** | 18.18 GiB *(weights)* / ~20.5 GiB | **12.55 GiB** | **10.30 GiB** | 20.3–24.7 GiB |
| **Runs on 16GB T4 GPU?** | No *(BF16 only, TP=1)* | **Yes (`9.03 GiB` @ 4K)** | **Yes (`6.78 GiB` @ 4K)** | **Yes (`load_in_4bit`: `6.8 GiB`)** |
| **Top-1 Agreement vs. BF16** | **100.00%** *(max diff 0.0078)* | **99.86%** *(1,471/1,473)* | **98.78%** *(1,455/1,473)* | **100.00% *(Ref)*** |
| **1. Short Routing** *(293 tok, K=2)* | **30.01 ms** *(9,763 tok/s)* | 32.82 ms *(8,929 tok/s)* | 31.69 ms *(9,245 tok/s)* | 43.23 ms *(6,778 tok/s)* |
| **2. Typed Decisions** *(736 tok, K=5)* | **50.29 ms** *(14,635 tok/s)* | 62.40 ms *(11,795 tok/s)* | 61.80 ms *(11,909 tok/s)* | 63.58 ms *(11,576 tok/s)* |
| **3. When2Call** *(1,068 tok, K=1)* | **66.06 ms** *(16,167 tok/s)* | 87.78 ms *(12,167 tok/s)* | 89.34 ms *(11,955 tok/s)* | 77.77 ms *(13,733 tok/s)* |
| **4. PhishNChips** *(1,303 tok, K=9)* | **75.86 ms** *(17,176 tok/s)* | 108.55 ms *(12,004 tok/s)* | 109.97 ms *(11,849 tok/s)* | 90.32 ms *(14,426 tok/s)* |
| **5. SafeAgents Judge** *(2,217 tok, K=2)* | **125.85 ms** *(17,616 tok/s)* | 181.70 ms *(12,201 tok/s)* | 183.50 ms *(12,082 tok/s)* | 138.06 ms *(16,058 tok/s)* |
| **Batch `B=4` Typed Decisions** *(2,944 tok, 20 dec)* | **152.39 ms** *(19,319 tok/s · 131.2 dec/s)* | 248.50 ms *(11,847 tok/s · 80.5 dec/s)* | 245.20 ms *(12,006 tok/s · 81.6 dec/s)* | 175.85 ms *(16,742 tok/s · 113.7 dec/s)* |
---
## GGUF Quantization Accuracy Retention & Speed Benchmarks
### 1. Decision Index 10-Benchmark Test-Set Accuracy Retention (`BF16` vs. `Q8_0` vs. `Q4_K_M`)
Evaluated across a stratified 10-benchmark Decision Index test suite (**`203` multi-constraint requests / `1,473` scored schema questions** spanning `choice`, `noul`, and `score` heads), `FastBrain-9B-Q8_0.gguf` (`9.66 GB`) is **virtually lossless (`99.86%` Top-1 decision agreement with native `bfloat16`, `-0.03 pp` score delta)**, while `FastBrain-9B-Q4_K_M.gguf` (`6.73 GB`, with `dec.*` head preserved in `Q8_0`) retains **`98.78%` Top-1 agreement** and **`-0.20 pp`** calibrated 10-task mean:
| Benchmark / Metric | PyTorch bfloat16
*(18.82 GB)* | GGUF Q8_0
*(9.66 GB)* | GGUF Q4_K_M
*(6.73 GB · Q8 Head)* |
| :--- | :---: | :---: | :---: |
| **Top-1 Agreement vs. BF16** *(1,473 q)* | 100.00% *(Ref)* | **99.86%** *(1,471/1,473)* | **98.78%** *(1,455/1,473)* |
| **Probability MAE (\|p_q - p_bf16\|)** | 0.00000 | **0.00203** *(0.20%)* | **0.00387** *(0.39%)* |
| **10-Task Mean (Single-Ckpt Raw)** | **82.37%** | **82.34%** *(-0.03 pp)* | **82.56%** *(+0.19 pp)* |
| **10-Task Mean (Calibrated)** | **83.68%** | **83.65%** *(-0.03 pp)* | **83.48%** *(-0.20 pp)* |
| ├─ **1. BFCL** *(AST Tool Selection)* | 100.00% | **100.00%** *(0.00)* | **100.00%** *(0.00)* |
| ├─ **2. ToolRet** *(20-Tool Reranking)* | 73.15% | **73.70%** *(+0.55)* | **75.60%** *(+2.45)* |
| ├─ **3. API-Bank** *(Stateful Tool Call)* | 95.00% | **95.00%** *(0.00)* | **95.00%** *(0.00)* |
| ├─ **4. BANKING77** *(77-Way Intent)* | 100.00% | **100.00%** *(0.00)* | **100.00%** *(0.00)* |
| ├─ **5. CLINC150+OOS** *(Intent + OOS)* | 93.94% | **93.94%** *(0.00)* | **93.94%** *(0.00)* |
| ├─ **6. Home Appliances** *(Control)* | 90.00% | **90.00%** *(0.00)* | **90.00%** *(0.00)* |
| ├─ **7. BRIGHT** *(Reasoning Retrieval)* | 41.93% | **41.00%** *(-0.93)* | **40.31%** *(-1.62)* |
| ├─ **8. Amazon ESCI** *(Relevance)* | 73.89% | **73.89%** *(0.00)* | **75.79%** *(+1.90)* |
| ├─ **9. PhishNChips** *(9-Probe Sec)* | 85.00% | **85.00%** *(0.00)* | **85.00%** *(0.00)* |
| └─ **10. When2Call** *(4-Way Gov)* | 75.00% *(70.83%)* | **75.00%** *(0.00)* | **72.92%** *(-2.08)* |
### 2. Measured Hardware Footprint & Inference Speed (`llama-server b11430`)
Because `FastBrain-9B` scores all schema questions and candidate options in a **single non-autoregressive prefill forward pass (`output_tokens = 0`)**, `llama-server` achieves **8,900–12,100+ input tokens/sec** (`31–109 ms` end-to-end HTTP latency) on server GPUs:
| Workload / Metric | Q8_0.gguf
*(PRO 6000)* | Q4_K_M.gguf
*(PRO 6000)* | Q8_0.gguf
*(Tesla T4 16G)* | Q4_K_M.gguf
*(Tesla T4 16G)* | PyTorch BF16
*(PRO 6000)* |
| :--- | :---: | :---: | :---: | :---: | :---: |
| **Model Load Time** | **2.56 s** | **2.00 s** | **5.43 s** | **5.10 s** | ~11.5 s |
| **Peak GPU VRAM** | **12.55 GiB**
*(16K ctx)* | **10.30 GiB**
*(16K ctx)* | **9.03 GiB**
*(4K ctx)* | **6.78 GiB**
*(4K ctx)* | 20.3–24.7 GiB |
| **Intent + OOS**
*(293 tok, K=2)* | **32.82 ms**
*(8,928 tok/s)* | **31.69 ms**
*(9,245 tok/s)* | **349.32 ms**
*(839 tok/s)* | **342.98 ms**
*(854 tok/s)* | ~35.0 ms |
| **When2Call**
*(1,068 tok, K=1)* | **87.78 ms**
*(12,167 tok/s)* | **89.34 ms**
*(11,955 tok/s)* | **1,149.8 ms**
*(929 tok/s)* | **1,154.3 ms**
*(925 tok/s)* | ~92.0 ms |
| **PhishNChips**
*(1,303 tok, K=9)* | **108.55 ms**
*(12,004 tok/s)* | **109.97 ms**
*(11,849 tok/s)* | **1,430.8 ms**
*(911 tok/s)* | **1,440.5 ms**
*(905 tok/s)* | ~115.0 ms |
| Format / Precision Tier | Weight Footprint | Peak VRAM (`4K`–`16K` ctx) | Minimum Recommended Hardware |
| :--- | :---: | :---: | :--- |
| **`FastBrain-9B-Q4_K_M.gguf`** *(4-bit + `Q8_0` Head)* | `6.73 GB` | `~6.8 – 10.3 GiB` | **8GB–12GB GPU / Tesla T4 (16GB) / Mac M1–M4** |
| **PyTorch `load_in_4bit=True`** *(NF4 + `fp16` Head)* | `~5.5 GB` | `~6.2 – 6.8 GiB` | **Tesla T4 (16GB) / RTX 3060–4060 (8GB+)** |
| **`FastBrain-9B-Q8_0.gguf`** *(Full 8-bit GGUF)* | `9.66 GB` | `~9.0 – 12.6 GiB` | **12GB–16GB GPU / Tesla T4 (16GB) / Mac 16GB+** |
| **PyTorch `load_in_8bit=True`** *(INT8 + `bf16` Head)* | `~9.6 GB` | `~11.2 GiB` | **Tesla T4 (16GB) / RTX 4070 Ti (12GB+)** |
| **Native `bfloat16` (`safetensors` / `BF16.gguf`)** | `18.82 GB` (`18.16 GB`) | `20.3 – 24.7 GiB` | **NVIDIA L4 (24GB) / A100 / RTX 3090–4090 / PRO 6000** |
---
## Repository Structure
| File | Size | Description |
| :--- | :---: | :--- |
| `model-00001-of-00004.safetensors` .. `00004` | `18.59 GB` | Full `9B` backbone weights (`bfloat16`, in-place merged, no LoRA required) |
| `joint_head.safetensors` & `joint_head_config.json` | `232 MB` | `116M`-parameter `JointSchemaHead` weights (`122` tensors) and configuration |
| `FastBrain-9B-Q4_K_M.gguf` | `6.73 GB` | Recommended 4-bit GGUF (`dec.*` decision head, `ssm_`, and `attn_` preserved at `Q8_0`) |
| `FastBrain-9B-Q8_0.gguf` | `9.66 GB` | High-precision 8-bit GGUF (`ClefModel` architecture for `llama-server`) |
| `FastBrain-9B-BF16.gguf` | `18.16 GB` | Full-precision `bfloat16` GGUF (`ClefModel` architecture for `llama-server`) |
| `mmproj-FastBrain-9B-Q8_0.gguf` & `BF16.gguf` | `624 MB` / `922 MB` | Multimodal vision projector GGUF files for image/screenshot states |
| `joint_schema_model.py` | `25 KB` | `ClefModel`, `JointSchemaHead`, `load_release_model(load_in_4bit/8bit)`, and `systemone()` runtime |
| `eval_submissions/typescript_fastbrain-9b.json` | `901 KB` | Full 400-case / 2,000-decision Zero-Shot prediction trace for `LocalLLaMA/typed-decisions` ([Merged PR #58](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/58)) |
| `eval_submissions/typescript_fastbrain-9b-fitted.json` | `902 KB` | Full 400-case / 2,000-decision Fitted (`train`-calibrated) prediction trace for `LocalLLaMA/typed-decisions` ([PR #57](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/57)) |
| `eval_submissions/TypeScript__FastBrain-9B*.jsonl` | `110 KB` | Full 661-call (`v2`) binary & 7-way category prediction traces for `harness-hub/safeagents-judge-bench` ([Merged PR #1](https://huggingface.co/TypeScript/FastBrain-9B/discussions/1)) |
| `.eval_results/*.yaml` | `2.3 KB` | Official Hugging Face Hub benchmark evaluation metadata (`typed-decisions` & `safeagents-judge-bench v2`) |
| `README.md` & `README_zh.md` | `32 KB` | Full technical documentation in English and Simplified Chinese |
| `config.json`, `tokenizer.json`, `processor_config.json` | `11 MB` | Standard Hugging Face model, tokenizer, and processor configurations |
| `assets/*.png` | `1.2 MB` | High-resolution technical benchmark and calibration charts |