cagataydev/loopl-0.8b-4bit

loopl 0.8B · v1.2

An agent that lives on your phone. loopl runs the model, the loop and the tools on the device. It talks, uses the phone, asks before it acts — and works with the network off. This repo is the MLX 4-bit export — what the loopl app downloads — of the 0.8B loopl model, a Qwen3.5 post-tuned to run loopl's agent loop well, know who it is, and read images.

It uses the phone — every tool call is a row.	It asks before anything leaves the device.	Show it a photo. It reads it on-device.

_{Real captures from the free iPhone app.}

What loopl is

• A Swift SDK — Agent(model:tools:) and a small, honest loop: model → tools → model until it answers. Tool errors go back to the model (it fixes the call instead of repeating it); anything that leaves the device waits for a tap. Runtimes for MLX, llama.cpp and Apple's on-device model; hooks, checkpoints, structured output, tracing.
• A free iPhone app built on it — pick a model, talk to it in airplane mode, let it use the phone (torch, haptics, translation, speech, image generation, other apps, memory, HTTP), show it photos, record a take, and train it on your own conversations from the phone. No account, nothing collected.
• These models — the same Qwen3.5 the app offers, post-tuned so the loop, the identity and the tool manners are in the weights, not in a system prompt you cannot see.

loopl.dev · Get it on TestFlight · github.com/cagataycali/loopl

This model

probe	this model	previous (v1)	base Qwen3.5 4-bit
agent-SDK knowledge quiz (first 472 questions, exact-answer)	74	59	50
tool calls, native template dialect	9/9	7/9	6/9
tool calls, loopl JSON dialect	7/9	7/9	2/9
identity (5 classic probes, with system prompt)	5/5	4/5	4/5
identity · bio facts (8, with system prompt)	8/8	—	—
identity · no system prompt (13)	12/13	—	—

Scored by train/eval.py on the 4-bit MLX export (greedy). The quiz asks for exact file-level facts about an agent SDK's source — hard for every small model; the delta over base is the point. Tool probes count a well-formed call with the right arguments. identity_nosys is the one that matters in the app: the system prompt there is the user's to edit.

Vision: the base's tower is frozen and kept — 153 vision_tower.* tensors in the MLX export, so the app shows the photo button.

Use it today

In the loopl app (iPhone). Install from TestFlight → Models → the loopl models section lists this size (no account, no token) → download. The app sees the vision tower and shows the photo button; the system prompt is yours to edit — identity and tool manners are in the weights.

On a Mac with MLX (vision included):

pip install -U mlx-vlm
python -m mlx_vlm.generate --model cagataydev/loopl-0.8b-4bit \
  --image photo.jpg --prompt "What is in this picture? Then tell me who built you." --max-tokens 200

With llama.cpp: the GGUF Q4_K_M export (cagataydev/loopl-0.8b-GGUF) is not public yet; export your own from the bf16 weights with llama.cpp's convert_hf_to_gguf.py --no-mtp (text only — the converter has no vision path).

From Swift with the loopl SDK — the snippet below is docs/start.md § "Run the loop" in the loopl repo, compile-checked in CI against the package (products Loopl + LooplRuntimes); spec is this repo's ModelSpec and dir the downloaded folder:

import Loopl
import LooplRuntimes
import Foundation

func chat(_ spec: ModelSpec, at dir: URL) async throws {
    let model = MLXModel()                                       // Metal — a real device
    try await model.load(spec, from: dir) { _ in }

    let agent = Agent(model: model,
                      tools: [CurrentTimeTool(), CalculatorTool()],
                      systemPrompt: "You are a concise assistant on the user's phone.")

    for try await event in agent.stream("What time is it in Istanbul, and what is 23 × 47?") {
        switch event {
        case .text(let t):            print(t, terminator: "")
        case .toolStarted(let use):   print("\n→ \(use.name)")
        case .toolResult(let r):      print("← \(r.content)")
        default: break
        }
    }
}

Keep post-tuning it. The bf16 weights (cagataydev/loopl-0.8b) are the base for continual post-tuning: in the app, Models › Train your own takes a loopl model as the base and trains on your own shared conversations (train/loopl_sft.py --base cagataydev/loopl-0.8b); on a Mac they load with transformers ≥ 5 as Qwen3_5ForConditionalGeneration. Export your own quantisation with mlx_vlm.convert -q (keeps vision) — mlx_lm.convert silently drops the tower.

Recipe

base	Qwen/Qwen3.5-0.8B (vision tower frozen, visual.* never trained)
data	cagataydev/loopl-train (private) @ 47d27b44 · 13704 rendered rows / 37,226,229 tokens · both tool dialects · oversample c=2
method	full fine-tune of the language model · lr 2e-05 · batch 2×8 · max_len 4096 · 2.0 epochs, best-epoch checkpoint kept
eval loss	ep1 1.856, ep2 1.910 → best 1.856 (eval split of the same dataset revision)
compute	a100-large (Hugging Face Jobs), 9143 s train · job 6ac5e6e2404719ba37665442
stack	transformers 5.19.0 · trl 1.14.2 · peft 0.21.2
exports	MLX 4-bit via mlx_vlm.convert -q (153 vision tensors of 847, 625 MB) · GGUF Q4_K_M via llama.cpp --no-mtp (529 MB, text only)

Script: train/loopl_sft.py — the same single file the loopl app launches when you tap Models › Train your own on your own conversations. Assistant turns that seed a bad tool call carry weight: 0 so the model learns the recovery, not the mistake. Tokenizer files are normalised to the base's (Qwen2Tokenizer, chat_template.jinja with tools) so Swift loaders accept them; train/check_mlx_repo.py gates every export on that plus the vision triad (vision_config ⇔ vision_tower.* ⇔ preprocessor_config.json).

Limitations

• A 0.8B model: fluent and well-behaved in the loop, not an encyclopedia. It will get arithmetic and obscure facts wrong; give it tools (calculator, http, recall) and it does better.
• Identity holds in most no-prompt probes (see the scores), not all; a one-line system prompt ("You are loopl…") makes it consistent.
• The GGUF export is text-only. Vision needs the MLX export (or the bf16 weights).
• Trained on English plus a little Turkish; other languages are the base's.
• Knowledge about agent SDKs is file-level and dated to the dataset revision; it does not know your repo.

Data and privacy

Every training row is agent-synthesised from public sources (the owner's public GitHub repos and the agent-SDK source they build on), critic-reviewed (score ≥ 4/5 kept) and filtered by a denylist for secrets, private names, phone numbers and addresses — see the dataset card. No user conversations from the app are in this model.

License

Apache-2.0, inherited from the Qwen3.5 base. The fine-tune, exports and dataset are © Cagatay Cali, same license.

The family

Six public repos, one recipe. The app lists the three MLX rows under Models › loopl models (2B is the sweet spot for speed on an iPhone 15/16; 4B is the most knowledgeable; 0.8B fits anywhere). Every bf16 repo is a valid --base for the next round.

	MLX 4-bit · vision · what the app downloads	bf16 · the base to keep training
0.8B	cagataydev/loopl-0.8b-4bit · 625 MB ← this repo	cagataydev/loopl-0.8b · 1.71 GB
2B	cagataydev/loopl-2b-4bit · 1.72 GB	cagataydev/loopl-2b · 4.43 GB
4B	cagataydev/loopl-4b-4bit · 3.03 GB	cagataydev/loopl-4b · 9.08 GB

All of them: the loopl collection.

Links

• Site and docs — loopl.dev · SDK: agent loop, tools, human in the loop, models
• App — Get it on TestFlight (iOS 18+, free, public beta)
• Code — github.com/cagataycali/loopl · issues · training: github.com/cagataycali/loopl-train
• Models — the loopl collection · data: cagataydev/loopl-train (private)
• Built by Cagatay Cali — @cagataycali