Instructions to use mohitkrconnect/heed-tasks-only-long-qwen3-0.6b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mohitkrconnect/heed-tasks-only-long-qwen3-0.6b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="mohitkrconnect/heed-tasks-only-long-qwen3-0.6b")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mohitkrconnect/heed-tasks-only-long-qwen3-0.6b")
model = AutoModelForCausalLM.from_pretrained("mohitkrconnect/heed-tasks-only-long-qwen3-0.6b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mohitkrconnect/heed-tasks-only-long-qwen3-0.6b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mohitkrconnect/heed-tasks-only-long-qwen3-0.6b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mohitkrconnect/heed-tasks-only-long-qwen3-0.6b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mohitkrconnect/heed-tasks-only-long-qwen3-0.6b

SGLang

How to use mohitkrconnect/heed-tasks-only-long-qwen3-0.6b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "mohitkrconnect/heed-tasks-only-long-qwen3-0.6b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mohitkrconnect/heed-tasks-only-long-qwen3-0.6b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "mohitkrconnect/heed-tasks-only-long-qwen3-0.6b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mohitkrconnect/heed-tasks-only-long-qwen3-0.6b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mohitkrconnect/heed-tasks-only-long-qwen3-0.6b with Docker Model Runner:
```
docker model run hf.co/mohitkrconnect/heed-tasks-only-long-qwen3-0.6b
```

Heed tasks-only with long notes, Qwen3-0.6B (EXP-049): the biggest single step

Research checkpoint, not the app model. The model inside Heed is heed-tasks-qwen3-0.6b. This model writes task text only (no buckets), was never judged, and was never run on the sealed test.

This is the best tasks-only model of the study. It was trained on one consistent set of new Claude Sonnet 5.5 labels plus 2,697 long, messy synthetic notes. It went to the phone as the app model on and was replaced by later models that also write a bucket.

Why this model exists

The first phone model, heed-tasks-only-qwen3-0.6b (EXP-038), wrote tasks like "Maybe call the bank", because its older labels kept hedges. EXP-049 asked two questions:

Do today's Sonnet 5.5 labels, written under one frozen prompt, fix that and improve task finding?
Do 2,697 long messy notes (120 to 199 tokens) help on long notes, and how many epochs does the model need?

Both answers were yes (decisions D109 to D112, D115 to D117). On real notes the word-overlap F1 went from 60.9 to 81.7 on the same key, the largest single improvement in the study.

Prompt and answer format (exact)

Raw text, no chat template, no system prompt. There is no bucket list in the prompt. The prompt ends with a newline after Tasks:.

Note: <the note text>
Tasks:

The answer is a JSON list of strings followed by EOS. An invented example:

{"tasks":["Call the bank at 5","Buy milk"]}

A note with nothing to do gives {"tasks":[]}. Spoken times stay inside the task text. No bucket, time field or reminder is written. Journal items stay in the target.

transformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "mohitkrconnect/heed-tasks-only-long-qwen3-0.6b"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, dtype=torch.bfloat16)

note = "call the bank at five and buy milk on the way home"
prompt = f"Note: {note}\nTasks:\n"   # raw prompt, no chat template

ids = tok(prompt, return_tensors="pt").input_ids
out = model.generate(ids, max_new_tokens=512, do_sample=False)  # greedy, as evaluated
print(tok.decode(out[0, ids.shape[1]:], skip_special_tokens=True))

Weights are bf16. The duplicated tied lm_head was removed before upload (596,049,920 parameters).

Training

Setting	Value
Run	EXP-049 `q06_long_s0`, epoch 3 (of 5 epochs, seed 0)
Base	`Qwen/Qwen3-0.6B-Base`, full fine-tune, 596,049,920 parameters
Data	6,914 notes, one row each (4,217 older notes + 2,697 long notes), no punctuation variants. Validation: 451 notes (371 older + 80 long). The 617 sealed-test notes were never put on the training machine
Labels	Claude Sonnet 5.5 under one frozen labelling prompt (EXP-046; EXP-048 for the long notes), temperature 0. Hedges like "Maybe" are no longer kept. No per-note human check
Recipe	lr 1e-5, AdamW (weight decay 0), 5% warm-up then cosine, batch 8 × 2 = 16, max length 1,024, loss on answer tokens only, bf16 autocast, gradient checkpointing
Steps / time	2,165 steps (5 epochs), 18.86 minutes, best validation loss 0.470 (step 866), peak 14.1 GB
Epoch choice	By word-overlap F1, not loss. Best epochs across the four runs were 3, 2, 3, 2
Hardware / cost	1 × RTX 4090. About 85 minutes of GPU for the whole experiment (both seeds and the arm without long notes), about $1.3 at the quoted $0.90 per hour (derived estimate, not an invoice)

Training notes (6,914):

Source	Notes	What it is
Simulated notes	1,108	Written by Claude Sonnet 5.5, Gemini and GPT-5.4 in my style
HeedSet	1,263 + 399	Synthetic, written by Claude Opus; 1,263 labelled earlier by Sonnet 5.5, 399 relabelled in EXP-046
Notes I planned	781	Planned by me and generated with ChatGPT
Claude brain dumps	480	Messy notes written by Claude Opus (220) and Sonnet (260)
Curated template notes	36	Picked by hand from a template-generated draft
Real notes	140	Real notes from me and five friends
Typed notes	10	Typed by me
Long synthetic notes	2,697	Written by Sonnet 5.5, 70% "scattered" personas; 3,550 writer attempts gave 2,858 in range; no near-copies

Long notes are 39% of the notes, 62% of the tasks and about 60% of the training tokens. 96% of them open with "okay", "so" or "umm", a known habit of the writer model. English only.

Results

Read this first. These are quick word-overlap F1 scores against Claude Sonnet 5.5's labels, not judged scores. They are not comparable with the blind-judge numbers on the app model card, nor with the older-key numbers of the first phone model (91.2 there is on the old key). The old model re-scored on the new key reads 79.7.

Batched generation, validation set, epoch kept:

	Old model (EXP-038, re-scored on the new key)	Same recipe, new labels, no long notes (mean of 2)	This model (`long` seed 0, ep3)	`long` seed 1 (ep2)	`long` mean of 2
Yardstick (199 notes)	79.7	89.6	91.0	90.2	90.6
Real notes (59)	60.9	77.7	80.7	82.8	81.7
HeedSet (140)	85.3	93.1	94.1	92.5	93.3
Older notes (371)	76.2	85.9	86.9	86.4	86.6
Long notes (80)	not scored	74.7	84.2	84.0	84.1
All (451)	not scored	81.6	85.8	85.5	85.6

Exact lists on real notes: old 35.6%, new labels 40.7% and 49.2%, with long notes 50.8% and 50.8%. On long notes: new labels 6.2% and 6.2%, with long notes 23.8% and 20.0%.

One-at-a-time scoring on 451 notes, the way a phone runs, gave the same picture (means 90.5 / 81.6 / 84.0 / 85.5 for yardstick / real / HeedSet / older, against 90.6 / 81.7 / 84.1 / 85.6 batched). Answers differed on 26 of 96 notes, F1 84.5 against 84.8.

Honest reading. The old model's 91.2 and 77.9 were on the old key. The same answers on the new key score 79.7 and 60.9, so the gain of +9.9 and +16.8 is partly the new models copying Sonnet's style. Only a judge could separate "better" from "more similar", and this experiment was never judged.

Pre-registered predictions: P49.3 (long notes gain at least 5 points) true, at +9.4. P49.4 (within 1 point on older notes) true, at +0.7. P49.5 (best epoch 3 or later in all runs) false (3, 2, 3, 2).

On the phone

Int8 conversion (litert-torch, about 585 MiB) on 30 validation notes, OnePlus CPH2717 (Dimensity 8300): 0 errors; median 2.1 s on real notes, 4.1 s on long notes and 1.3 s on others; slowest 13 s; 1,152 MB; 35 °C. Same list as the pod on 15 of 30 notes (wording differs between int8 and bf16). F1 on those 30 notes: 81.2 int8 against 83.4 on the pod, too few to measure quantisation. On a Samsung M32 (3.73 GB) the same 30 notes ran with 0 errors, 341 s in total, median 10.1 s. The int8 file is not part of this repository.

What it fed, and why it was superseded

It fed the decision to relabel everything with one Sonnet prompt (D109) and to add long notes (D111, D112). The phone ran it from. The next experiments (EXP-050, EXP-054, EXP-061) added the bucket, trained every note in both written and lowercase forms, and added targeted notes.

Limitations

Tasks only: no bucket, time field or reminder.
Quick word-overlap scores against one model's labels; judged score not run, sealed test not run.
No lowercase or unpunctuated training, so it was not prepared for speech-to-text output.
Long notes are synthetic and "sound unlike phone notes". The real-note slice is only 59 notes.
Trained on 150 real notes from me and five friends; memorisation was not tested.
English only.

Links

App and website: https://heed-xi.vercel.app
Source code: https://github.com/t3chw/Heed_app
App model: https://huggingface.co/mohitkrconnect/heed-tasks-qwen3-0.6b
Base model: https://huggingface.co/Qwen/Qwen3-0.6B-Base

Data: the synthetic part of the training, validation and test notes, with all five systems' answers and the judge's verdicts on the public test notes, is in mohitkrconnect/heed-voice-notes-to-tasks. My real notes and my friends' are not published.

License

Apache-2.0, the same as the base model. The training labels and most training notes were produced with Claude, Gemini and GPT models. Check those providers' terms before using this model to build a competing general-purpose model.