CTRL-0.6B

The in-browser build of the Expedient agent: a QLoRA adapter on Qwen/Qwen3-0.6B (Apache-2.0), trained on the same reviewed knowledge base as CTRL-2B.

It exists to make the demo runnable inside a browser tab: the Q4_K_M GGUF is 378 MB instead of 1.45 GB, and Qwen's architecture is supported by llama.cpp WASM builds, so CTRL-0.6B runs client-side with no server.

▶ Try it: CTRL-0.6B demo Space

This is not a replacement for CTRL-2B. Use CTRL-2B where quality matters; use CTRL-0.6B where the model has to ship to the client.

Measured results

Number-fact recall (gold numbers present in the answer) and precision (answer numbers present in gold — invented numbers are the hallucination signal). 26 held-out questions, greedy decoding.

Arm	Recall	Precision
Base Qwen3-0.6B, closed-book	0.692	—
CTRL-0.6B, closed-book	0.692	0.846
CTRL-0.6B, open-book (retrieved excerpts)	0.962	1.000

Retrieval hit@3: 26/26 (mean gold rank 0.0). The 94-row production-shape check on the full 939-passage index also lands 94/94.

Read that carefully. Closed-book recall is identical to the base model — at 0.6B the adapter learns voice and format but cannot store facts. All the factual reliability comes from retrieval. For comparison, CTRL-2B reaches 0.772 closed-book recall and 0.865 precision at 2.5B.

Note the encouraging part: open-book precision is 1.000 — with excerpts in front of it, CTRL-0.6B invented no numbers across the eval set.

Training

• QLoRA r16 / α32, lr 2e-4 cosine, effective batch 16, 4 epochs, 211 steps (~14 min on an RTX 4050 Laptop 6 GB), NF4 double-quant, bf16 compute
• Corpus: 939 QA rows (258 first-party reviewed + 681 promoted through automated source-page verification; see CTRL-2B for the triage method)
• Assistant-only supervision; trained format is Qwen ChatML with an empty think block: <|im_start|>assistant\n<think>\n\n</think>\n\n{answer}

Files

Path	What
ctrl-06b-v1-q4_k_m.gguf	378 MB Q4_K_M, the browser build
adapter/	LoRA weights + tokenizer
kb/	The retrieval payload the demo ships: kb_corpus.jsonl (939 passages), kb_emb.json (passage vectors), q_emb.json (query vectors for the curated questions)

Retrieval vectors are precomputed with BAAI/bge-small-en-v1.5 (CLS pooling, L2-normalised) so the browser only has to run a dot product — no embedder download and no in-browser embedding pass.

Serving

Any llama.cpp build runs the GGUF. In the browser the demo uses @wllama/wllama 2.3.1; note that wllama 2.x bundles an older llama.cpp, and Qwen-architecture GGUFs load there while some newer architectures do not.

Governance

Base model Apache-2.0; adapter ships Apache-2.0. Corpus sources are Expedient first-party KB drafts plus vendor documentation paraphrased and source-verified against live pages. Rights in the underlying vendor content remain with the publishers; this release is intended for Expedient-internal support use.