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Model: art87able/unstuck-qwen2.5-0.5b-steps
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+  - unstuck
+  - task-breakdown
+  - adhd
+  - lora
+  - build-small-hackathon
+  - small-models
+language:
+  - en
+---
+
+# Unstuck — Qwen2.5-0.5B fine-tuned for ADHD task breakdowns
+
+A tiny (0.5B) instruct model fine-tuned to turn one overwhelming task into **tiny, timed, categorised steps** in the exact JSON schema used by [Unstuck](https://huggingface.co/spaces/build-small-hackathon/unstuck) (HF Build Small Hackathon, Backyard AI track).
+
+It powers Unstuck's `UNSTUCK_BACKEND=finetuned` path — a fully local, serverless-free option that runs the same `generate(prompt) -> str` seam as the app's other backends.
+
+## What it does
+
+Given an overwhelming task, it returns only:
+
+```json
+{"steps":[{"text":"Open a trash bag and collect visible rubbish","category":"admin","est_minutes":5}, ...]}
+```
+
+- each step a single concrete action starting with an imperative verb,
+- one category from `admin · creative · errand · deep-work`,
+- a positive-integer minute estimate, **never above 25** (a hard app rule).
+
+## How it was built
+
+- **Base:** `Qwen/Qwen2.5-0.5B-Instruct`.
+- **Data:** 130 schema-valid breakdowns **distilled** from a strong serverless model (`Qwen/Qwen3-30B-A3B-Instruct-2507` on Nebius Token Factory), each filtered through Unstuck's own validator so only on-contract examples survive. Purely synthetic — no user data.
+- **Method:** LoRA (r=16, α=32, dropout 0.05; q/k/v/o projections), 3 epochs, lr 2e-4, merged into the base. Final train loss ≈ 0.21.
+- **Compute:** a single **Modal** A10G GPU. Full pipeline (distill → train → verify): [`scripts/finetune/`](https://github.com/art87able/unstuck/tree/main/scripts/finetune).
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tok = AutoTokenizer.from_pretrained("art87able/unstuck-qwen2.5-0.5b-steps")
+model = AutoModelForCausalLM.from_pretrained("art87able/unstuck-qwen2.5-0.5b-steps")
+# Feed it Unstuck's breakdown prompt (see unstuck.prompts.breakdown_prompt) and parse the JSON.
+```
+
+Or in the app: `UNSTUCK_BACKEND=finetuned python app.py`.
+
+## Limitations
+
+A 0.5B model specialised for one narrow JSON task — it is not a general chat model. Outputs should still be schema-validated (the app does one repair retry on failure). Estimates are starting points; Unstuck recalibrates them to *your* real timings client-side.
+
+- **Source:** https://github.com/art87able/unstuck
+- **Live app:** https://huggingface.co/spaces/build-small-hackathon/unstuck

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config.json

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+{
+  "_name_or_path": "Qwen/Qwen2.5-0.5B-Instruct",
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
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+  "max_position_embeddings": 32768,
+  "max_window_layers": 21,
+  "model_type": "qwen2",
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+  "num_hidden_layers": 24,
+  "num_key_value_heads": 2,
+  "rms_norm_eps": 1e-06,
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+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.46.3",
+  "use_cache": true,
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+  "vocab_size": 151936
+}

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+  "temperature": 0.7,
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+  "top_p": 0.8,
+  "transformers_version": "4.46.3"
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special_tokens_map.json

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+}