FabricAI/Fabric1.6

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Fabric 1.6

Fabric 1.6 is a 35-billion-parameter Mixture-of-Experts (MoE) reasoning model developed by Fabric AI, with approximately 3 billion parameters activated per token. It is a native multimodal, agentic model built on a hybrid Gated DeltaNet + Gated Attention architecture, with explicit chain-of-thought reasoning, a native 262,144-token context window, and built-in Multi-Token Prediction (MTP) for up to 50% faster generation.

Fabric 1.6 is designed for agentic use in harnesses such as OpenCode, Pi Agent, Hermes Agent and other OpenAI-compatible tool-calling environments, and offers the option to preserve thinking context from past messages across long multi-turn sessions.

1. Key Features

• Hybrid Architecture: Gated DeltaNet (linear attention) layers interleaved with Gated Attention layers inside a 256-expert MoE transformer — sub-quadratic scaling with full attention capacity where it matters.
• Native Long Context: 262,144 tokens natively, extensible up to 1,010,000 tokens.
• Multi-Token Prediction (MTP): predicts multiple future tokens per step for up to 50% faster generation.
• Native Multimodality: accepts text, image and video inputs within the same model.
• Explicit Reasoning: produces an internal chain of thought before answering; reasoning is exposed in a structured format that can be streamed and stored.
• Agentic by Design: reliable structured tool-calling, long-horizon task execution, and preserved thinking context across turns.

2. Model Summary

Architecture	Hybrid Gated DeltaNet + Gated Attention, Mixture-of-Experts (MoE)
Total Parameters	35B
Activated Parameters	~3B
Number of Layers	40
Layer Layout	10 × (3 × (Gated DeltaNet → MoE) → 1 × (Gated Attention → MoE))
Hidden Dimension	2048
Gated DeltaNet	32 value heads, 16 QK heads, head dimension 128
Gated Attention	16 Q heads, 2 KV heads, head dimension 256, RoPE dim 64
Number of Experts	256
Selected Experts per Token	8 routed + 1 shared
Expert Intermediate Dimension	512
Vocabulary Size	248,320
Context Length	262,144 (extensible to 1,010,000)
Multi-Token Prediction	1 MTP layer (up to 50% faster generation)
Vision Encoder	27-layer ViT, hidden 1152, patch 16, temporal patch 2
Modality	Text, Image, Video
Precision	BF16 (this repo)

3. Datasets Used to Train

Fabric 1.6 was developed from the Qwen3.5-35B-A3B-Base foundation through continuous pre-training followed by post-training (supervised fine-tuning and reinforcement-learning-based alignment).

Pre-training was performed primarily on a large, in-house proprietary synthetic dataset spanning code, mathematics and reasoning, complemented by open reasoning corpora:

• OpenThoughts3-1.2M — 1.2M high-quality reasoning traces across mathematics, science, coding and general problem solving.
• OpenR1-Math-220k — 225k mathematical problems with think-style solutions.

Post-training instruction data combines permissively licensed open corpora with proprietary data:

• smoltalk2 — an Apache-2.0 SFT subset (~340k examples) covering multilingual instruction following, multi-turn reasoning, tool-calling traces, system chats and table understanding.
• hermes-function-calling-v1 — structured tool-calling traces.
• oasst2 — curated, reviewed conversational chains.
• maple — a proprietary instruction and reasoning corpus developed in-house by Fabric AI (CC-BY-4.0).

In total, approximately 12 billion tokens were processed across the pre-training and post-training stages. Knowledge cutoff: July 2026.

4. Evaluation Results

Fabric 1.6 was evaluated on 22 benchmarks with greedy decoding (temperature 0).

Category	Benchmark	Score
Math & Reasoning	AIME25	92.8
	AIME26	93.1
	HMMT26	83.2
	IMOAB	79.2
	M500	84.8
Science & Knowledge	GPQA	86.7
	GPQA-D	84.9
	HLE	21.4
	MMLU-P	85.6
	MMLU-R	93.5
	C-Eval	92.3
Coding	LCB6	80.2
	SWEB-V	72.9
	SWEB-P	50.1
	IFEval	93.09
General Reasoning	GSM8K-Pt	95.73
Agentic Tools	TAU3	67.2
	MMMU-P	74.10
	RWQA	85.4
	MCP-A	62.8
	WS	60.3
	MV-mini	86.6

5. Deployment

Fabric 1.6 runs on the following inference engines with built-in MTP (multi-token prediction) support:

vLLM

Install:

uv pip install vllm --torch-backend=auto

For deployment across an 8-GPU node (with built-in MTP support):

vllm serve FabricAI/Fabric1.6 --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --speculative-config '{"method":"qwen3_next_mtp","num_speculative_tokens":2}'

For tool use:

vllm serve FabricAI/Fabric1.6 --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder

SGLang

Install:

uv pip install sglang[all]

For deployment across an 8-GPU node (with built-in MTP support):

python -m sglang.launch_server --model-path FabricAI/Fabric1.6 --port 8000 --tp-size 8 --mem-fraction-static 0.8 --context-length 262144 --reasoning-parser qwen3 --speculative-algo NEXTN --speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4

For tool use:

python -m sglang.launch_server --model-path FabricAI/Fabric1.6 --port 8000 --tp-size 8 --mem-fraction-static 0.8 --context-length 262144 --reasoning-parser qwen3 --tool-call-parser qwen3_coder

6. Model Usage

Fabric 1.6 always has thinking enabled and returns reasoning_content alongside the answer. The model was trained in preserved thinking history mode: for multi-turn conversations and tool calls, pass the complete assistant message returned by the API back to messages as-is — including reasoning_content and tool_calls, not just content — so that reasoning from earlier turns remains available to later ones.

7. License

The model weights are released under the Apache License 2.0.

8. Citation

If you use Fabric 1.6 in your work, please cite it as:

@misc{fabric1.6,
    title = {{Fabric1.6}: Agentic Open Model for Enterprises},
    url = {https://huggingface.co/FabricAI/Fabric1.6},
    author = {{Fabric AI}},
    month = {August},
    year = {2026}
}

9. Contact

For questions, collaborations or access requests, contact the Fabric AI research team at research@fabricai.co.uk.