Fine-tune Gemma Extraction Model Adapter
i have opend this and connetced:https://colab.research.google.com/notebook#scrollTo=UUjeeVxRHEVz&fileId=https%3A//huggingface.co/google/gemma-3-270m-it.ipynb, guide me step by step what i should do: The untuned Gemma model missed the extraction gate. It parsed 2 of 30 held-out excerpts (6.7%) and matched 4 of 90 dimension-plus-polarity pairs (4.4%). The required gate is 95% parse and 80% accuracy. No tuned adapter was measured, so the shipping model stays the untuned 270M file. This is not committed.
The held-out set has 30 authored examples, and the training set has 100. They do not share text. Sampling was temperature 0, seed 0, and one schema retry. The report is ml/reports/baseline-270m.json. uv run pytest passed, 118 tests. No SerpApi search was spent.
From your side:
- Open
ml/tune_extraction.ipynbon a free Colab T4 and let it run, or say that Colab is unavailable. The base model is gated, so Colab needs a read-onlyHF_TOKENin secrets. Uploadml/extraction/train.jsonlandml/extraction/held_out.jsonlif Colab cannot see the repository. - Paste the two printed Colab estimate lines. Do not publish any score that is not in the baseline report or in that output.
The notebook trains a short adapter and prints an estimate. It does not replace the shipping model.
CHECK: questions_this_turn=0 step=P3.5 autonomy=A1 packet=none stage=C
You opened the official Gemma notebook, not the extraction tuner. I’ll read the handoff and ml/tune_extraction.ipynb so the steps match what this repo actually expects.
You opened the official Gemma demo notebook. Close that tab. The run that matters is the local file ml/tune_extraction.ipynb, which is only on this machine, so Colab cannot load it from GitHub.
Do these in order. Stay on the free T4. Do not paste the token into a cell or into chat.
1. Unlock the model. Open google/gemma-3-270m-it and accept the license if the page asks. Then create a read token at huggingface.co/settings/tokens. A read token does not start billing.
2. Upload the notebook. In Colab, choose File → Upload notebook and pick:
/home/kernel-kain/Documents/Github/happen/ml/tune_extraction.ipynb
3. Add the secret. In the new notebook, open the key icon in the left sidebar. Name it exactly HF_TOKEN, paste the read token as the value, and turn Notebook access on.
4. Switch to a free T4. Runtime → Change runtime type → T4 GPU. If Colab only offers a paid GPU, stop and tell me Colab is unavailable. Do not buy compute.
5. Upload the two data files. Open the folder icon, upload into /content, and keep these exact names:
ml/extraction/train.jsonlml/extraction/held_out.jsonl
6. Run every cell. Runtime → Run all. The first data cell should print 100 training rows 30 held-out rows. Training is 40 steps, then it scores the 30 held-out excerpts. Expect several minutes on a T4.
7. Paste back only these two lines from the last cell:
That estimate does not replace the shipping model. The untuned baseline stays 6.7% parse and 4.4% accuracy until a later local check.
If a cell fails, paste the error text and stop there. Typical causes: the secret toggle is off, the Gemma license is not accepted, the runtime is not a T4, or the two .jsonl files are not in /content.
Hacktoberfest Weekend Challenge is still open, with about 17 hours left, closing October 5 at 06:59 UTC. Best Use of Gemma is a $200 category. I can audit Happen against the rubric after this Colab paste.
what opions should i slect: Create new Access Token Token type Fine-grained Read Write This cannot be changed after token creation. Token name happen-google/gemma-3-270m-it Presets Read-Only Inference Write CI/CD Full Access Custom
Select Read as the token type. Leave the name as happen-google/gemma-3-270m-it.
Read already covers downloading google/gemma-3-270m-it after you have accepted that model's license. You do not need a preset for this token type, so ignore Read-Only, Inference, Write, CI/CD, Full Access, and Custom.
Then create the token, copy it once, and put it in the Colab secret named HF_TOKEN with notebook access turned on. Do not paste it into a cell or into chat.
i am not able to edit it , i tsay read only for the file i uuplaoded, also another collab notbeook is open for this model what to do with it
The uploaded notebook is view-only until Colab saves your own copy. I’ll check the file, then tell you exactly which notebook to keep and which to close.
Close the official Gemma notebook. Keep working only in a copy of ml/tune_extraction.ipynb.
The other notebook. That tab is Google’s demo for google/gemma-3-270m-it. Do not run it. Use Runtime → Disconnect and delete runtime so it releases the free T4, then close the tab. The HF_TOKEN secret stays on your account, so the Happen notebook can still use it.
The read-only file. Colab opened your upload as a view. The notebook in the repo is a normal file. Make your own editable copy:
- Stay on the uploaded
tune_extractiontab. - Choose File → Save a copy in Drive.
- Colab opens a new tab named
Copy of tune_extraction. That tab is the one you edit and run. - Close the read-only tab.
Then, only in the copy:
- Confirm the key-icon secret
HF_TOKENhas notebook access turned on. - Choose Runtime → Change runtime type → T4 GPU.
- Open the folder icon and upload
train.jsonlandheld_out.jsonlinto/content. - Choose Runtime → Run all.
Paste back only the two lines that start with Colab estimate.
i have removed all teh conetent from opened nb and put copyied all cocnetnt from this file into my notebook and runned all cells one by one what should i do next, how should i knwo i did things right?
The run is finished when the last cell prints two lines and no cell shows a red error. Paste those two lines here.
Check the notebook from top to bottom. Each of these should have appeared:
- The install cell finished with no error.
- The data cell printed
100 training rows 30 held-out rows. - The model cell printed a trainable-parameter line.
- The training cell printed four loss lines (
0,10,20,30) and thensaved /content/happen-adapter. - The last cell printed:
Those two lines are the result. Copy them exactly and paste them in your next message. Leave the rest of the cell output in Colab.
A correct run is one that reached that last print. The percentages can be low. This notebook only estimates the adapter. It does not replace the shipping model.
If the last cell is still running, wait for it. Scoring 30 excerpts is the slow part. If any cell is red, paste that error text and stop.
i go this error: --------------------------------------------------------------------------- SecretNotFoundError Traceback (most recent call last) /tmp/ipykernel_2608/758404441.py in <cell line: 0>() 8 from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig 9 ---> 10 login(token=userdata.get("the key i added in this"), add_to_git_credential=False) 11 12 def load_split(name):
/usr/local/lib/python3.13/dist-packages/google/colab/userdata.py in get(key) 66 raise TimeoutException(key) 67 if not resp.get('exists', False): ---> 68 raise SecretNotFoundError(key) 69 if not resp.get('access', False): 70 raise NotebookAccessError(key)
SecretNotFoundError: Secret "the key i added in this" does not exist. ;; do i have to keep key somehwere else or shoul di ha put key diretly in this cell
Put the token in Colab’s secret list, not in the cell. The cell only looks up a secret by the name HF_TOKEN.
That error means the lookup name in the code does not match a secret. Change that line back to exactly this:
HF_TOKEN is the name. It is not the token.
Then set the secret:
- Click the key icon in the left sidebar.
- If a secret is named something else, remove it.
- Add a secret whose name is exactly
HF_TOKEN. - Paste the read token into the value field.
- Turn on Notebook access for that secret.
Run that cell again. A successful login continues into the data load and prints 100 training rows 30 held-out rows.
If the real token was typed into the cell, delete that text from the cell, revoke the token at huggingface.co/settings/tokens, create a new Read token, and store only the new one in the HF_TOKEN secret. Do not paste the token here.
i have not added any file, where should i add the fiels:FileNotFoundError Traceback (most recent call last) /tmp/ipykernel_2608/1429119010.py in <cell line: 0>() 16 raise FileNotFoundError(name) 17 ---> 18 train_rows = load_split("train.jsonl") 19 held_rows = load_split("held_out.jsonl") 20 print(len(train_rows), "training rows", len(held_rows), "held-out rows")
/tmp/ipykernel_2608/1429119010.py in load_split(name) 14 if candidate.is_file(): 15 return [json.loads(line) for line in candidate.read_text().splitlines() if line.strip()] ---> 16 raise FileNotFoundError(name) 17 18 train_rows = load_split("train.jsonl")
FileNotFoundError: train.jsonl
Upload both files into Colab’s /content folder, then rerun the cell that just failed.
- In Colab, click the folder icon on the left sidebar.
- Click the upload icon (a file with an upward arrow).
- Select these two files from this machine:
/home/kernel-kain/Documents/Github/happen/ml/extraction/train.jsonl/home/kernel-kain/Documents/Github/happen/ml/extraction/held_out.jsonl
- Leave them in
/content. Their names must staytrain.jsonlandheld_out.jsonl. Do not put them in a subfolder. - If Colab warns that the files disappear when the runtime restarts, choose OK.
- Run the failed cell again.
A successful load prints 100 training rows 30 held-out rows. Then keep running the remaining cells in order.
where is this content folder?
/content is inside the Colab page, not a folder on your computer. It is the file list on the left side of that notebook.
- Look at the far left edge of the Colab tab.
- Click the folder icon. It sits below the key icon you used for
HF_TOKEN. - A panel opens titled Files. The top of that list is
/content. - Click the upload icon at the top of that panel (a page with an upward arrow).
- Choose
train.jsonlandheld_out.jsonlfromml/extraction/on this machine. - After the upload, both names should appear in that Files panel.
Then run the failed cell again. It should print 100 training rows 30 held-out rows.
this cell is executing from last 5 min what is wrong: model.eval() parsed = 0 correct = 0 dimensions = ("conversation", "short_wait", "seating") for row in held_rows: prompt = tokenizer.apply_chat_template( [{"role": "user", "content": prompt_for(row)}], tokenize=False, add_generation_prompt=True, ) inputs = tokenizer(prompt, return_tensors="pt").to(model.device) with torch.no_grad(): output = model.generate(**inputs, max_new_tokens=384, do_sample=False) reply = tokenizer.decode(output[0][inputs["input_ids"].shape[1] :], skip_special_tokens=True) start = reply.find("{") end = reply.rfind("}") try: payload = json.loads(reply[start : end + 1]) if start >= 0 and end > start else None except json.JSONDecodeError: payload = None if not isinstance(payload, dict): continue parsed += 1 predicted = {} for signal in payload.get("signals", []): if isinstance(signal, dict) and signal.get("dimension") in dimensions: predicted[signal["dimension"]] = signal.get("polarity") gold = {signal["dimension"]: signal["polarity"] for signal in row["signals"]} for dimension in dimensions: if dimension in gold: correct += predicted.get(dimension) == gold[dimension] else: correct += dimension not in predicted
parse_rate = parsed / len(held_rows) accuracy = correct / (len(held_rows) * 3) print(f"Colab estimate parse rate {parse_rate:.1%}") print(f"Colab estimate dimension-plus-polarity accuracy {accuracy:.1%}") print("Baseline parse rate 6.7%. Baseline accuracy 4.4%.") print("Paste these two Colab lines back. Do not publish any other score.") print("Do not replace the shipping model from this estimate.")
Nothing is wrong. That cell is scoring all 30 held-out excerpts before it prints anything, so five minutes of silence is normal.
Leave it running. Each excerpt can generate up to 384 tokens, and the two Colab estimate lines appear only after the last one finishes. On a free T4 this often takes about 10 to 20 minutes.
It is still working if the cell shows a running indicator and the top-right RAM or GPU meter is moving. When it finishes, paste only these two lines:
its been 10 min now
Ten minutes is still inside the normal range. Leave the cell running.
The loop prints nothing until all 30 excerpts are scored, so a quiet cell at this point is expected. Wait until the two Colab estimate lines appear, or until about 20 minutes have passed.
It is still working if the cell’s running indicator is on and the top-right RAM or GPU meter keeps changing. Paste those two lines when they show up. If the cell is still running with no output after 20 minutes, tell me and stop there.
Colab estimate parse rate 6.7% Colab estimate dimension-plus-polarity accuracy 3.3% Baseline parse rate 6.7%. Baseline accuracy 4.4%. Paste these two Colab lines back. Do not publish any other score. Do not replace the shipping model from this estimate.
The Colab run finished, and the adapter did not beat the untuned baseline. I’ll record those two lines in the progress log and check what the next step is.
The run completed correctly. The estimate is worse than the untuned baseline, so the shipping model stays the untuned 270M file. I’ll record that in the progress log.
You did the Colab run correctly. The adapter estimate is worse than the untuned model, so the shipping model stays the untuned 270M file.
| Parse rate | Dimension-plus-polarity accuracy | |
|---|---|---|
| Untuned baseline | 6.7% | 4.4% |
| Colab adapter estimate | 6.7% | 3.3% |
| Required gate | 95% | 80% |
An adapter is kept only if a later local check gains at least five percentage points without more invalid outputs. This estimate does not. It is not copied into the repo, and it does not replace ml/reports/baseline-270m.json.
Disconnect the Colab runtime with Runtime → Disconnect and delete runtime, then close the tab. Nothing else is needed for tuning.
The next step is the public Render deploy. That has not been started.
but fintetuning is needed, whta to do to make output better? If nothing can be done should i like pivot this project in diffrernt direction? i have only 8hrs of implementation time left.
The short adapter did not help, so another training run is a poor use of the time left. I’ll check how the extractor actually fails and what the product still requires before recommending a direction.
Keep this project. Another fine-tune will not produce a reliable extractor in the time you have, and replacing Gemma would throw away the app you already built.
Fine-tuning was the stretch goal. It is already settled: the untuned model is the better one, at 4.4% accuracy against the adapter estimate of 3.3%. The required extraction gate is 95% parse and 80% accuracy. The baseline parsed 2 of 30 excerpts, and 28 of 30 were malformed. The short Colab run did not fix that. A longer one still has to be merged, quantized, and measured locally before it can ship, and the locked rule is to keep the untuned 270M file when tuning does not improve the result.
The product already has the correct behavior for a model this weak. Gemma runs locally. Python accepts a span only when it is valid and an exact quote from the review. If that leaves too little evidence, the page returns no winner. That refusal is the result, not a broken demo. The labeled winner screen at /?layout=sample is there to show the layout, and it is marked as a sample.
Spend the 8 hours on the unfinished public app:
- Disconnect the Colab runtime. Do not start another training run, and do not switch to Gemma 3 1B tonight.
- Deploy the existing frontend and backend on Render.
- Leave Find the moment on the synthetic fixture, and leave the live path honest when the model keeps no usable spans.
- Use the last block for the friend walkthrough and the DEV post. The post should say the 270M model missed the gate and the validator stopped it from inventing a restaurant.
A grammar could force the JSON shape, but it would not teach this model which polarity is supported. Repairing broken JSON and treating it as evidence is outside the plan. A keyword extractor cannot be described as Gemma.
Community Wisdom: I Kept Retrying a Local Model Into the Right Shape. Turns Out I Didn't Have To Retry At All.
Source: Naitik Kapatel Tags:
llm,node,typescript,gbnfA local llama.cpp model kept missing a tiny label format, and retries only paid for a second generation. A token grammar made the illegal output unreachable. That job was one of five labels on a 3B model. Happen’s failure is a multi-field evidence schema on a 270M model, so the same trick would clean the shape and still leave the accuracy gap.
Hacktoberfest Weekend Challenge is still open and closes October 5 at 06:59 UTC. Best Use of Gemma is a $200 category, and this result is a real entry if the public page and the writeup are honest. I can start the Render deploy next, or draft that post.