Modelpublic

clawdiaonduty/clawdia-qwen3-4b

sourceHugging Faceapache-2.0updated 4mo agoView on Hugging Face

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Model Card

Clawdia-Qwen3-4B

LoRA fine-tune of Qwen/Qwen3-4B for on-device use inside Clawdia. Bigger sibling of the 1.7B build — same training data, better instruction-following, fewer hallucinations on Clawdia-specific UI questions.

This is the recommended local model for systems with 8+ GB RAM. The Q5KM GGUF is ~2.7 GB; pair it with Clawdia's bundled llama.cpp runtime.

For a smaller (1.2 GB) variant, see [clawdiaonduty/clawdia-qwen3-1.7b](https://huggingface.co/clawdiaonduty/clawdia-qwen3-1.7b).

Files

File	Format	Size	Use
`qwen3-4b-clawdia.Q5_K_M.gguf`	GGUF, Q5KM	2.7 GB	Recommended — best quality / speed trade-off
`qwen3-4b-clawdia.Q4_K_M.gguf`	GGUF, Q4KM	2.3 GB	Smaller, slightly worse
`qwen3-4b-clawdia.f16.gguf`	GGUF, f16	7.5 GB	Full precision (for further fine-tuning or reference)

How to use

Inside Clawdia (recommended)

Settings → Local Inference → pick Clawdia-Qwen3 4B Q5_K_M. Clawdia downloads to ~/.clawdia/local-inference/models/ and runs it via the bundled llama.cpp runtime.

llama.cpp directly

bash

llama-completion \
  --model qwen3-4b-clawdia.Q5_K_M.gguf \
  --jinja \
  -sysf system_prompt.txt \
  -p "log $14.50 for lunch /no_think" \
  --temp 0.0 -n 280

Two critical flags:

—--jinja: enables the embedded Qwen3 chat template (tool calls depend on this).
—Append /no_think to user messages — Qwen3's base thinking mode wastes tokens before reaching tool calls.

What it does well

Probe	Output
`"who are you?"`	"Clawdia. macOS assistant. Runs on-device by default. I can send messages, schedule reminders, log expenses, take screenshots, run CLIs, and keep a memory wiki."
`"how do I switch to claude opus?"`	"Settings → Providers → Anthropic → paste API key → save. Then pick a Claude Opus model in the chat-header dropdown."
`"how do I enable iMessage?"`	Numbered steps for Full Disk Access + Automation + watch handle + restart.
`"where is my data stored?"`	"Everything lives at `~/.clawdia/`. The chat history, memory wiki, scheduled tasks, settings, and model downloads are there."
`"lunch was 30 for 2 of us and we split"`	`math(expression="30 / 2")` first, then logs the $15 share.
`"remind me on the 28th of every month to pay rent"`	`scheduled_task_create({"name":"Pay rent","schedule":{"every":1,"unit":"month","dayOfMonth":28,"atTime":"09:00"},"audience":"user","category":"Finance"})`
`"rm -rf my home directory"`	"Not running that — it would wipe your home folder. If you meant deleting a specific subfolder, point it out and I'll do that."

Training

—Base: Qwen/Qwen3-4B
—Adapter: LoRA rank 32, alpha 32, dropout 0.05 — applied to q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj on the top 16 transformer layers
—Data: 1,662 hand-authored multi-turn dialogs across 25 categories (finance, memory, iMessage, Telegram/WhatsApp, scheduled tasks, pantry, proactive, todos/habits/journal, setup/safety, edge cases, indirect/proactive offers, goal-aware reasoning, math splits, packages/orders, web/news, MCP tools, memory CLI, Clawdia self-knowledge, Clawdia UI / don't-lie discipline)
—Mask: train_on_responses_only — loss only on assistant tokens
—Schedule: AdamW, lr 2e-4, cosine decay, 5% warmup, 4 epochs (~430 steps), effective batch 16, max_seq_length=6144
—Hardware: 1× Modal H100, ~29 min wall-clock
—Loss: averaged 0.40 (train), best eval 0.565 at epoch 1.92 (final eval climbed — slight overfit; use earlier checkpoint if needed)

Known rough edges

—Tool-name drift in some finance/memory calls: occasionally emits finance_add_expense instead of canonical finance(action="add_expense"). Less frequent than the 1.7B variant but still happens. Targeted fix in next iteration.
—Identity string drift: When asked "what model are you?" the 4B variant still answers "Clawdia-Qwen3-1.7B" — the training data was authored for the 1.7B build. Cosmetic.

License

Apache 2.0 — inherited from Qwen/Qwen3-4B.