Delete chan_finetune_colab.ipynb

chan_finetune_colab.ipynb

@@ -1,126 +0,0 @@
-# 🎯 Well-Tuned — fine-tune & publish your own model (step by step)
-
-Goal: publish a small **fine-tuned** model on Hugging Face and use it inside the
-app. We fine-tune **Qwen3-1.7B** on one focused skill the app already performs —
-turning a Chan-theory *raw read* into a crisp long-hold *AI summary* — then plug
-it back in as the Translator sub-agent. This earns **🎯 Well-Tuned** and
-strengthens **🐜 Tiny Titan** (a 1.7B model doing the job well).
-
-Everything you need is already in the app and in this `finetune/` folder. Total
-hands-on time ≈ 1 hour, most of it waiting.
-
----
-
-## Why this approach (read once)
-
-- **Self-sourcing data.** You don't hunt for a dataset — the app *generates* it.
-  Each time you run the Signals **AI summary**, the (raw read → narrative) pair
-  is saved to `/data/dataset/pairs.jsonl`. A few hundred pairs is plenty.
-- **Single skill, small model.** Narrowing the task lets 1.7B match or beat a
-  generic 4B, which is the whole point of the hackathon's "think small".
-- **Honest fit.** The fine-tune does exactly what the app uses it for — judges
-  can see the loop close.
-
----
-
-## Step 1 — Capture training data (in the app, ~15 min)
-
-1. Open the Space → **Signals** → **Run analysis**.
-2. For each ticker: pick it in the dropdown, click **🤖 AI summary**. Each run
-   auto-saves one training pair (you'll see them counted on the Model tab).
-3. Vary it: change the ticker pool (large caps, a few volatile names, an ETF),
-   re-run analysis, summarize again. Aim for **150–500 pairs**.
-   - Tip: the **Auto Research** reports and the **rotation narrative** also
-     exercise the models, but only the Signals AI summary is captured for SFT
-     (it's the cleanest single-skill pair).
-4. **Model tab → 🎯 Fine-tuning dataset → ⬇ Export dataset (JSONL)**. It writes
-   `/data/dataset/chan_sft_<timestamp>.jsonl` and tells you the pair count.
-
-### Get the file onto your computer
-The file lives on the Space's `/data` bucket. Easiest way to grab it:
-```bash
-pip install huggingface_hub
-huggingface-cli login          # your token
-# list what's in the Space's persistent storage isn't exposed directly, so
-# instead download via the Space's file browser, OR re-export to the repo:
-```
-Simplest reliable path: in the **Model tab** the export prints the full path;
-open the Space's **Files** isn't enough for /data, so use the Space terminal if
-you enabled "Dev mode", **or** temporarily add a `gr.File` download — if you
-want that button, tell me and I'll wire it in. Otherwise the Colab notebook
-also accepts the JSONL via direct upload (Step 2 there).
-
-> If you'd rather not fish the file out of `/data`, I can add a one-click
-> **"Download dataset"** button to the Model tab — say the word.
-
----
-
-## Step 2 — Train on a free Colab T4 (~25–40 min)
-
-1. Go to <https://colab.research.google.com> → **File → Upload notebook** →
-   pick `finetune/chan_finetune_colab.ipynb` from this project.
-2. **Runtime → Change runtime type → T4 GPU → Save.**
-3. Run the cells top to bottom:
-   - Cell 1 installs Unsloth (fast, low-memory LoRA).
-   - Cell 2 asks you to **upload your `chan_sft_*.jsonl`**.
-   - Cells 3–5 load Qwen3-1.7B in 4-bit, attach LoRA adapters, and train
-     (3 epochs; 10–20 min for a few hundred rows).
-   - Cell 6 prints a sample generation so you can eyeball quality.
-4. If the sample looks off, raise `num_train_epochs` to 4–5 and re-run Cell 5.
-
----
-
-## Step 3 — Convert to GGUF + publish (~5 min, same notebook)
-
-1. Cell 7: `login()` — paste a **write** token (HF → Settings → Access Tokens).
-2. Set `HF_USER = 'ranranrunforit'`. The cell calls
-   `model.push_to_hub_gguf(REPO, tokenizer, quantization_method='q8_0')`, which
-   merges the LoRA, converts to GGUF via llama.cpp, and uploads to
-   `ranranrunforit/chan-compass-qwen3-1.7b-gguf`.
-3. Open the new repo → **Files** → note the exact `.gguf` filename (e.g.
-   `chan-compass-qwen3-1.7b-gguf.Q8_0.gguf` — Unsloth's name may differ).
-
----
-
-## Step 4 — Wire it into the app (1 line, ~2 min)
-
-In `llm_local.py`, find the commented block in `MODEL_ZOO` and edit it to match
-your repo id and the **exact** filename from Step 3:
-
-```python
-"Chan-Tuned Qwen3-1.7B · my fine-tune": (
-    "ranranrunforit/chan-compass-qwen3-1.7b-gguf",
-    "chan-compass-qwen3-1.7b-gguf.Q8_0.gguf"),   # ← exact filename
-```
-
-Optionally make it the fast sub-agent's default so the app uses it everywhere:
-
-```python
-FAST_MODEL = "Chan-Tuned Qwen3-1.7B · my fine-tune"
-```
-
-Commit, reboot the Space. The Model tab now lists your fine-tune, and it loads
-through llama.cpp like any other GGUF. **🎯 Well-Tuned achieved.**
-
----
-
-## Checklist for the badge
-
-- [ ] Dataset exported from the app (`chan_sft_*.jsonl`).
-- [ ] LoRA trained on Qwen3-1.7B (Colab T4).
-- [ ] Merged + converted to GGUF, **published to a public HF repo**.
-- [ ] App's `MODEL_ZOO` points at that repo; it loads and answers.
-- [ ] (Nice) Mention the fine-tune in your demo + blog (`BLOG_DRAFT.md`).
-
-## Troubleshooting
-
-- **Colab OOM on T4:** keep `load_in_4bit=True`, batch size 2, max_seq_length
-  2048 (defaults here). Don't bump LoRA `r` above 32.
-- **`push_to_hub_gguf` slow/fails:** re-run the cell; conversion downloads
-  llama.cpp once. If it still fails, save the merged model with
-  `model.save_pretrained_merged(...)` and convert locally with
-  `llama.cpp/convert_hf_to_gguf.py` (also documented in Unsloth's README).
-- **Model loads but talks oddly:** too few pairs or too many epochs. 200+ pairs,
-  3 epochs is the sweet spot. Keep temperature low (the app uses 0.2 here).
-- **Space can't find the file:** the filename in `MODEL_ZOO` must match the repo
-  exactly (case-sensitive), and the repo must be public (or set HF_TOKEN).