Initial upload: Tiny Aya SQL agent (Lexsi hackathon, Track 3)
Browse files- MANIFEST.json +34 -0
- README.md +131 -0
- adapter_config.json +51 -0
- adapter_model.safetensors +3 -0
- prompt_example.json +10 -0
- training/token-ledger.json +318 -0
- training/train-b.log +530 -0
- training/train-report.json +972 -0
- training/training-set.json +639 -0
MANIFEST.json
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README.md
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---
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license: cc-by-nc-4.0
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base_model: CohereLabs/tiny-aya-global
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library_name: peft
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pipeline_tag: text-generation
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language:
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- en
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tags:
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- text-to-sql
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- agent
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- tool-use
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- lora
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- qlora
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- agenttune
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- auditkit
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datasets:
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- satish9177/tiny-aya-sql-agent-data
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- trl-lab/SQaLe-2-text-to-SQL-Queries
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---
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# Tiny Aya SQL agent (B)
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A LoRA adapter that turns Cohere's **Tiny Aya Global** into a tool-using agent for SQLite databases.
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This is **B**, the model submitted to the Lexsi AI Safety & Alignment Hackathon, Track 3 (Agentic
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Fine-tuning).
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- **How it was made:** QLoRA supervised fine-tuning on 1,500 verified episodes (the
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[A1500 adapter](https://huggingface.co/satish9177/tiny-aya-sql-agent-a1500)), plus one round of
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self-training on 306 of its own verified answers (rejection-sampling fine-tuning).
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- **Tools:** built with Lexsi **AgentTune**; every score is re-checked by **AuditKIT**.
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- Code, plans and full results: [github.com/satish9177/aya-db-agent](https://github.com/satish9177/aya-db-agent)
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- Data, trajectories and logs: [satish9177/tiny-aya-sql-agent-data](https://huggingface.co/datasets/satish9177/tiny-aya-sql-agent-data)
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## What it does
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The agent gets a question and four tools over a read-only SQLite database:
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| Tool | Does |
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|---|---|
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| `list_tables()` | lists the tables |
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| `describe_table(table)` | shows columns, foreign keys and three sample rows |
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| `run_sql(query)` | runs one read-only `SELECT` and previews up to 20 rows |
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| `submit_sql(query)` | submits the final query and ends the episode |
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- **Turn format:** it writes one tool call per turn as `[{"tool_name": ..., "parameters": {...}}]` and has
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**eight tool steps**.
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- **Prompt:** an exact example is in `prompt_example.json`.
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## Results (strict, final sets never used for any choice)
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Strict means the submitted query's full result equals the reference, and the query reads a real table.
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| Set | Base (untuned) | A1500 | **B (this model)** | gpt-6-luna (closed, same test) |
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|---|---:|---:|---:|---:|
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| SQaLe final (144, unseen databases) | 2.8% | 31.3% | **30.6%** | 66.0% |
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| BIRD Mini-Dev (493, never trained on) | 0.0% | 1.6% | **1.4%** | not run |
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| Shop retention, dev (40) | 0 | 40 | **40** | not run |
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| Shop retention, multi-hop (40) | 0 | 1 | **0** | not run |
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**Where it works:**
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- It reads the schema before querying.
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- Its submitted queries almost always run: 2 failures across all final sets, against 377 for the untuned
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model.
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- It is close to the closed model on simple one-table lookups (29 vs 31 of 36).
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**Where it does not:**
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- Questions over 3+ tables or needing several steps (1 of 51).
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- Large schemas (BIRD).
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- Recovering after a failed query; it never does.
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Running out of the eight tool steps is the main failure.
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## How to load
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```python
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import torch
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from transformers import AutoModelForCausalLM, BitsAndBytesConfig
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from peft import PeftModel
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base = AutoModelForCausalLM.from_pretrained(
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"CohereLabs/tiny-aya-global", revision="af89d219b53ed9b13b8a4645f9c8028973510324",
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dtype=torch.float16, device_map="auto",
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quantization_config=BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4",
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bnb_4bit_use_double_quant=True,
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bnb_4bit_compute_dtype=torch.float16))
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model = PeftModel.from_pretrained(base, "satish9177/tiny-aya-sql-agent")
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```
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**Tokenizer: load it from the base model's `tokenizer.json`**, as `ayadb.engine.official_tokenizer` does in
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the code repo. In transformers 5.x, `AutoTokenizer` replaces Tiny Aya's pre-tokenizer
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([huggingface/transformers#49192](https://github.com/huggingface/transformers/issues/49192)), and the token
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ids then differ from the ones used in training.
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To run the full agent loop and the scoring, use `ayadb.sqlrunner.evaluate_with_engine` from the code repo.
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## Training
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| | |
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|---|---|
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| Starting point | A1500 adapter, step 180 (QLoRA SFT on 1,265 SQaLe + 235 shop episodes) |
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| Self-training data | 306 verified, correct attempts by A1500 on 229 held-out SQaLe questions |
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| Quantization | 4-bit NF4, double quantization, fp16 compute |
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| LoRA | rank 16, alpha 32, dropout 0.05; q, k, v, o, gate, up and down projections |
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| Optimization | 2 passes, learning rate 1e-4, effective batch 16, 40 updates |
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| Loss | the model's own turns only; tool outputs are never trained |
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| Hardware | one Tesla T4 |
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| Adapter SHA256 | `90b04823368f50d6ef5ce85c171fffff317b11a3bf43a591cf3952f01b076837` |
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**Self-training vs a matched control:**
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- A token-matched control trained on teacher episodes for the same questions.
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- On dev, B beat it 4 tasks to 0 (p = 0.125) and scored 40/95, against 37 for A1500.
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- On the final sets, B and A1500 are the same within noise.
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Training records are in `training/`. Each run was preregistered and ran once; the plans are in the code
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repo's `experiments/` folder.
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## Limitations
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- **Accuracy:** it is weak on complex and multi-step SQL, and on large schemas.
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- **Recovery:** it does not recover from errors.
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- **Language:** English questions only.
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- **Closed-model comparison:** a cheap closed model (gpt-6-luna) scored far higher on the same test, at a
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lower cost per correct answer than this model on a rented T4.
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- **Use:** research and non-commercial use only (see the licence).
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## Licence and credits
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- **Licence:** CC-BY-NC-4.0, following the base model, **Tiny Aya Global** by Cohere Labs.
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- **AgentTune and AuditKIT** (Lexsi Labs) are source-available, non-commercial tools.
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- **SQaLe** by Cornelius Wolff, Daniel Gomm and Madelon Hulsebos (2026). Its card declares MIT.
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- **BIRD Mini-Dev** by Li et al. (NeurIPS 2024), CC BY-SA 4.0. It was used only for final evaluation.
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adapter_config.json
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{
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"alora_invocation_tokens": null,
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"alpha_pattern": {},
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"arrow_config": null,
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"auto_mapping": null,
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"base_model_name_or_path": "CohereLabs/tiny-aya-global",
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"bias": "none",
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"corda_config": null,
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"ensure_weight_tying": false,
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"eva_config": null,
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"exclude_modules": null,
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"kasa_config": null,
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"layer_replication": null,
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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha": 32,
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"lora_bias": false,
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"lora_dropout": 0.05,
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"lora_ga_config": null,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"monteclora_config": null,
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| 28 |
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"peft_type": "LORA",
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| 29 |
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"peft_version": "0.21.2",
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| 30 |
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"qalora_group_size": 16,
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"r": 16,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"q_proj",
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"o_proj",
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"gate_proj",
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"k_proj",
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"v_proj",
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"down_proj",
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"up_proj"
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],
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"target_parameters": null,
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"task_type": "CAUSAL_LM",
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| 45 |
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"trainable_token_indices": null,
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"use_bdlora": null,
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"use_dora": false,
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"use_qalora": false,
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"use_rslora": false,
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"velora_config": null
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}
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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:90b04823368f50d6ef5ce85c171fffff317b11a3bf43a591cf3952f01b076837
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size 120981200
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prompt_example.json
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[
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{
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"role": "system",
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| 4 |
+
"content": "You are a database analyst. Answer the question by exploring the read-only SQLite database with the tools below. Inspect tables and columns, execute SQL, and correct execution errors. Finish by calling submit_sql with the single query that answers the question. submit_sql ends the episode. Do not invent tool results or reference answers.\n\nYou can call these tools:\n[\n {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"list_tables\",\n \"description\": \"List every table in this SQLite database.\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {}\n },\n \"return\": {\n \"type\": \"string\"\n }\n }\n },\n {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"describe_table\",\n \"description\": \"Show one table's columns, foreign keys and up to three sample rows.\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"table\": {\n \"type\": \"string\",\n \"description\": \"Exact table name returned by list_tables.\"\n }\n },\n \"required\": [\n \"table\"\n ]\n },\n \"return\": {\n \"type\": \"string\"\n }\n }\n },\n {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"run_sql\",\n \"description\": \"Run a read-only SQLite SELECT, showing at most twenty rows (preview only).\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"query\": {\n \"type\": \"string\",\n \"description\": \"One read-only SQLite SELECT statement.\"\n }\n },\n \"required\": [\n \"query\"\n ]\n },\n \"return\": {\n \"type\": \"string\"\n }\n }\n },\n {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"submit_sql\",\n \"description\": \"Submit the final SQL query and immediately end this episode.\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"query\": {\n \"type\": \"string\",\n \"description\": \"One read-only SQLite SELECT answering the question.\"\n }\n },\n \"required\": [\n \"query\"\n ]\n },\n \"return\": {\n \"type\": \"string\"\n }\n }\n }\n]\n\nReply with only one JSON list containing one tool call and no other text: [{\"tool_name\": \"<name>\", \"parameters\": {\"<argument>\": <value>}}]. Use only the listed parameters. Tool observations arrive as user turns beginning with Tool result. Continue exploring as needed, then call submit_sql to finish."
|
| 5 |
+
},
|
| 6 |
+
{
|
| 7 |
+
"role": "user",
|
| 8 |
+
"content": "For each department that has at least one user with 'owner' permissions, determine the total number of slides currently in its associated playlists. The final result should only include departments where this slide count is strictly greater than the average slide count across all such qualifying departments. This requires a two-step aggregation: first, calculating the slide count per department by joining user, depa, play, and slide while filtering for owners; second, computing the average of those per-department counts and filtering the departments based on that intermediate average."
|
| 9 |
+
}
|
| 10 |
+
]
|
training/token-ledger.json
ADDED
|
@@ -0,0 +1,318 @@
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|
| 1 |
+
{
|
| 2 |
+
"optimizer_steps": 40,
|
| 3 |
+
"skipped_optimizer_steps": 0,
|
| 4 |
+
"microbatches": 612,
|
| 5 |
+
"input_tokens": 1073644,
|
| 6 |
+
"target_tokens": 99434,
|
| 7 |
+
"padded_tokens": 0,
|
| 8 |
+
"example_visits": 612,
|
| 9 |
+
"unique_examples_seen": [
|
| 10 |
+
"009f148ea99d8fa9041c45fe3348d01ea15a79ed4051d2181f191febf3fdc7cb",
|
| 11 |
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|
| 12 |
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|
| 13 |
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|
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|
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| 16 |
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|
| 17 |
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|
| 18 |
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|
| 19 |
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|
| 20 |
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|
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| 24 |
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|
| 316 |
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],
|
| 317 |
+
"last_global_step": 40
|
| 318 |
+
}
|
training/train-b.log
ADDED
|
@@ -0,0 +1,530 @@
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Failed to load /usr/local/lib/python3.12/site-packages/torchao/_C_cutlass_90a.abi3.so: Could not load this library: /usr/local/lib/python3.12/site-packages/torchao/_C_cutlass_90a.abi3.so
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85%|████████▌ | 34/40 [39:11<07:10, 71.70s/it]{"gpu_memory": {"time_utc": "2026-11-10T09:25:38.441164+00:00", "phase": "train-b", "event": "after_optimizer_step", "optimizer_step": 35, "skipped": false, "allocated_bytes": 3761213440, "reserved_bytes": 9456058368, "peak_allocated_bytes": 8374165504, "peak_reserved_bytes": 9456058368, "device_free_bytes": 6088228864, "device_total_bytes": 15828320256}}
|
| 461 |
+
{"optimizer_step": 35, "target_tokens": 89117}
|
| 462 |
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|
| 463 |
+
|
| 464 |
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88%|████████▊ | 35/40 [40:18<05:51, 70.37s/it]
|
| 465 |
+
|
| 466 |
+
|
| 467 |
+
{'loss': '0.02319', 'grad_norm': '0.1096', 'learning_rate': '5.45e-06', 'epoch': '1.784'}
|
| 468 |
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|
| 469 |
+
|
| 470 |
+
88%|████████▊ | 35/40 [40:18<05:51, 70.37s/it]{"gpu_memory": {"time_utc": "2026-11-10T09:26:45.860962+00:00", "phase": "train-b", "event": "after_optimizer_step", "optimizer_step": 36, "skipped": false, "allocated_bytes": 3761213440, "reserved_bytes": 9456058368, "peak_allocated_bytes": 8374165504, "peak_reserved_bytes": 9456058368, "device_free_bytes": 6088228864, "device_total_bytes": 15828320256}}
|
| 471 |
+
{"optimizer_step": 36, "target_tokens": 91548}
|
| 472 |
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|
| 473 |
+
|
| 474 |
+
90%|█████████ | 36/40 [41:25<04:37, 69.49s/it]
|
| 475 |
+
|
| 476 |
+
|
| 477 |
+
{'loss': '0.01488', 'grad_norm': '0.06782', 'learning_rate': '3.806e-06', 'epoch': '1.837'}
|
| 478 |
+
|
| 479 |
+
|
| 480 |
+
90%|█████████ | 36/40 [41:25<04:37, 69.49s/it]{"gpu_memory": {"time_utc": "2026-11-10T09:27:57.962323+00:00", "phase": "train-b", "event": "after_optimizer_step", "optimizer_step": 37, "skipped": false, "allocated_bytes": 3761248768, "reserved_bytes": 9456058368, "peak_allocated_bytes": 8374165504, "peak_reserved_bytes": 9456058368, "device_free_bytes": 6088228864, "device_total_bytes": 15828320256}}
|
| 481 |
+
{"optimizer_step": 37, "target_tokens": 94312}
|
| 482 |
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|
| 483 |
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|
| 484 |
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92%|█████████▎| 37/40 [42:37<03:30, 70.27s/it]
|
| 485 |
+
|
| 486 |
+
|
| 487 |
+
{'loss': '0.01667', 'grad_norm': '0.08227', 'learning_rate': '2.447e-06', 'epoch': '1.889'}
|
| 488 |
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|
| 489 |
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|
| 490 |
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92%|█████████▎| 37/40 [42:37<03:30, 70.27s/it]{"gpu_memory": {"time_utc": "2026-11-10T09:29:08.215742+00:00", "phase": "train-b", "event": "after_optimizer_step", "optimizer_step": 38, "skipped": false, "allocated_bytes": 3761230336, "reserved_bytes": 9456058368, "peak_allocated_bytes": 8374165504, "peak_reserved_bytes": 9456058368, "device_free_bytes": 6088228864, "device_total_bytes": 15828320256}}
|
| 491 |
+
{"optimizer_step": 38, "target_tokens": 96769}
|
| 492 |
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| 493 |
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|
| 494 |
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95%|█████████▌| 38/40 [43:48<02:20, 70.27s/it]
|
| 495 |
+
|
| 496 |
+
|
| 497 |
+
{'loss': '0.01294', 'grad_norm': '0.07646', 'learning_rate': '1.382e-06', 'epoch': '1.941'}
|
| 498 |
+
|
| 499 |
+
|
| 500 |
+
95%|█████████▌| 38/40 [43:48<02:20, 70.27s/it]{"gpu_memory": {"time_utc": "2026-11-10T09:30:12.050732+00:00", "phase": "train-b", "event": "after_optimizer_step", "optimizer_step": 39, "skipped": false, "allocated_bytes": 3761185792, "reserved_bytes": 9456058368, "peak_allocated_bytes": 8374165504, "peak_reserved_bytes": 9456058368, "device_free_bytes": 6088228864, "device_total_bytes": 15828320256}}
|
| 501 |
+
{"optimizer_step": 39, "target_tokens": 99157}
|
| 502 |
+
|
| 503 |
+
|
| 504 |
+
98%|█████████▊| 39/40 [44:51<01:08, 68.34s/it]
|
| 505 |
+
|
| 506 |
+
|
| 507 |
+
{'loss': '0.01309', 'grad_norm': '0.08377', 'learning_rate': '6.156e-07', 'epoch': '1.993'}
|
| 508 |
+
|
| 509 |
+
|
| 510 |
+
98%|█████████▊| 39/40 [44:51<01:08, 68.34s/it]{"gpu_memory": {"time_utc": "2026-11-10T09:30:18.906469+00:00", "phase": "train-b", "event": "after_optimizer_step", "optimizer_step": 40, "skipped": false, "allocated_bytes": 3760617472, "reserved_bytes": 9456058368, "peak_allocated_bytes": 8374165504, "peak_reserved_bytes": 9456058368, "device_free_bytes": 6088228864, "device_total_bytes": 15828320256}}
|
| 511 |
+
{"optimizer_step": 40, "target_tokens": 99434}
|
| 512 |
+
|
| 513 |
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|
| 514 |
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100%|██████████| 40/40 [44:58<00:00, 49.89s/it]
|
| 515 |
+
|
| 516 |
+
|
| 517 |
+
{'loss': '0.01022', 'grad_norm': '0.2026', 'learning_rate': '1.541e-07', 'epoch': '2'}
|
| 518 |
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100%|██████████| 40/40 [44:58<00:00, 49.89s/it]
|
| 521 |
+
|
| 522 |
+
|
| 523 |
+
{'train_runtime': '2699', 'train_samples_per_second': '0.227', 'train_steps_per_second': '0.015', 'train_loss': '0.02118', 'epoch': '2'}
|
| 524 |
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|
| 525 |
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100%|██████████| 40/40 [44:59<00:00, 49.89s/it]
|
| 527 |
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100%|██████████| 40/40 [44:59<00:00, 67.48s/it]
|
| 528 |
+
{"gpu_memory": {"time_utc": "2026-11-10T09:30:20.022761+00:00", "phase": "train-b", "event": "training_complete", "optimizer_step": 40, "allocated_bytes": 3760545280, "reserved_bytes": 9456058368, "peak_allocated_bytes": 8374165504, "peak_reserved_bytes": 9456058368, "device_free_bytes": 6088228864, "device_total_bytes": 15828320256}}
|
| 529 |
+
{"backend": "AgentTune.create_distill_trainer/Project.distill/AuditedSFTFactory", "global_step": 40, "loss": 0.021183327259495854, "runtime_seconds": 2713.295591436, "adapter_sha256": "90b04823368f50d6ef5ce85c171fffff317b11a3bf43a591cf3952f01b076837"}
|
| 530 |
+
{"step": "train-b", "completed": true}
|
training/train-report.json
ADDED
|
@@ -0,0 +1,972 @@
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| 2 |
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|
| 965 |
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|
| 966 |
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|
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|
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|
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"checkpoint_optimizer_saved": true,
|
| 970 |
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"loss_policy": "trained-positions-v1",
|
| 971 |
+
"gpu_memory_file": "runs/sql/b-selftrain-sql-20261109-01/training.gpu-memory.jsonl"
|
| 972 |
+
}
|
training/training-set.json
ADDED
|
@@ -0,0 +1,639 @@
|
|
|
|
|
|
|
|
|
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|
| 1 |
+
{
|
| 2 |
+
"source_file": "data/sqale/selftrain-v1/b.episodes.jsonl",
|
| 3 |
+
"source_sha256": "139266d6da19b03da42af2044ca587e6ce85cd3da95e66462106d4b819b35ac4",
|
| 4 |
+
"policy_sha256": "7012d5357fd4518bdcd2dcca83165a7560a28ccd6806113e08111499cc31362e",
|
| 5 |
+
"policy_version": "sqale-effective-inputs-v1",
|
| 6 |
+
"seed": 42,
|
| 7 |
+
"validation_share": 0.0,
|
| 8 |
+
"eligible_rows": 306,
|
| 9 |
+
"excluded": [],
|
| 10 |
+
"train_examples": 306,
|
| 11 |
+
"validation_examples": 0,
|
| 12 |
+
"train_episode_ids": [
|
| 13 |
+
"schema_006776_s2_q0_3e9c3af030-self-1",
|
| 14 |
+
"schema_006776_s1_q1_abb63dc388-self-1",
|
| 15 |
+
"schema_011690_s2_q0_7be33691df-self-1",
|
| 16 |
+
"schema_003562_s0_q0_5d9dfd8f0e-self-1",
|
| 17 |
+
"schema_017172_s3_q1_66324c198d-self-2",
|
| 18 |
+
"schema_008307_s1_q3_dd5113cca0-self-1",
|
| 19 |
+
"schema_005016_s3_q1_af843136a6-self-1",
|
| 20 |
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"schema_005016_s0_q5_30eef33f42-self-1",
|
| 21 |
+
"schema_011358_s1_q4_dd0eff1930-self-1",
|
| 22 |
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"schema_011385_s3_q1_a2196f108e-self-1",
|
| 23 |
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|
| 24 |
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|
| 25 |
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|
| 26 |
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|
| 27 |
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|
| 28 |
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|
| 29 |
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|
| 30 |
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|
| 31 |
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|
| 32 |
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|
| 33 |
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|
| 34 |
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|
| 35 |
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|
| 36 |
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|
| 37 |
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|
| 38 |
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|
| 39 |
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|
| 40 |
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|
| 41 |
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|
| 42 |
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|
| 43 |
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|
| 44 |
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|
| 45 |
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|
| 46 |
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|
| 47 |
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|
| 48 |
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|
| 49 |
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|
| 50 |
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|
| 51 |
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|
| 52 |
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|
| 53 |
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|
| 54 |
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|
| 55 |
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|
| 56 |
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|
| 57 |
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|
| 58 |
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|
| 59 |
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|
| 60 |
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|
| 61 |
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|
| 62 |
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|
| 63 |
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|
| 64 |
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|
| 65 |
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"schema_007486_s2_q4_eb68102d35-self-1",
|
| 66 |
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|
| 67 |
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|
| 68 |
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|
| 69 |
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|
| 70 |
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"schema_016173_s1_q6_70d7ed3030-self-1",
|
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