GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
visethchapman
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
Model Details
Model Provider
visethchapman
Model Tree
Qwen/Qwen2.5-Coder-1.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Given an English question about ERCOT hourly electricity demand or Houston weather, generates the Postgres SQL to answer it.
Example:
Q: What was peak hourly ERCOT demand in 2024?
SQL:
SELECT MAX(value) AS peak_demand FROM eia.demand WHERE region = 'ERCO' AND EXTRACT(YEAR FROM period) = 2024;
| Base | Qwen/Qwen2.5-Coder-1.5B-Instruct |
| Method | LoRA (fp16, no 4-bit quantization) |
| LoRA rank / alpha / dropout | 16 / 32 / 0.05 |
| Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| Trainable params | 18.5M / 1.56B (1.18%) |
| Training data | 280 synthetic Q/SQL pairs |
| Epochs | 3 |
| Effective batch size | 16 (per-device 2 × grad-accum 8) |
| Learning rate | 2e-4, cosine, 3% warmup |
| Max seq length | 1024 |
| Hardware | Kaggle T4 (free tier) |
| Wall clock | ~4.5 min |
| Final train / val loss | 0.11 / 0.11 |
| Val mean token accuracy | 97% |
The model expects the schema in the system prompt — it wasn't trained to memorize tables, only to map (schema + question) → SQL. A stub prompt causes table hallucination.
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel BASE = "Qwen/Qwen2.5-Coder-1.5B-Instruct"ADAPTER = "visethchapman/ercot-text2sql-qwen-1.5b-lora" tokenizer = AutoTokenizer.from_pretrained(BASE)model = AutoModelForCausalLM.from_pretrained(BASE, torch_dtype=torch.float16, device_map="auto")model = PeftModel.from_pretrained(model, ADAPTER) SYSTEM = """You are a Postgres SQL expert for ERCOT electricity-demand and Houston weather data. Schema:eia.demand(region, period, value, value_units) — region='ERCO'; period in UTC; value in MWhnoaa.daily_weather(station_id, obs_date, tmax_c, tmin_c, prcp_mm, awnd_ms) — Houston station; obs_date is local datenoaa.stations(station_id, name, state, nearest_eia_region) Notes: All demand is UTC. Houston weather is local date. For joins,cast period to local date: (period AT TIME ZONE 'America/Chicago')::date Return ONLY valid Postgres SQL. No explanation, no markdown fences.""" msgs = [ {"role": "system", "content": SYSTEM}, {"role": "user", "content": "What was peak hourly ERCOT demand in 2024?"},]inputs = tokenizer.apply_chat_template( msgs, return_tensors="pt", add_generation_prompt=True, return_dict=True,).to(model.device)out = model.generate(**inputs, max_new_tokens=256, do_sample=False)print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))Evaluated on the 12-question held-out eval set from
energy-text2sql
(result-row equivalence, sort-insensitive, float-tolerant):
| Model | Correct | Cost / run | Avg latency |
|---|---|---|---|
| Qwen2.5-Coder-1.5B (raw base, no fine-tune) | 2/12 (17%) | $0.00 | 5.26s |
| Qwen2.5-Coder-1.5B + LoRA (this model) | 6/12 (50%) | $0.00 | 3.12s |
| Claude Sonnet 4.5 (single-call baseline) | 12/12 (100%) | ~$0.05 | 4.6s |
| Claude Sonnet 4.5 multi-agent (LangGraph 4-node) | 12/12 (100%) | ~$0.10 |
Fine-tuning tripled the base-model score (2 → 6 correct) at zero incremental inference cost. The model handles simple aggregations and filters (peaks, totals, summer averages) but still fails on subtle SQL rules (GROUP BY with non-aggregated columns, alias-in-ORDER-BY) and cross-domain joins requiring TZ casts. Not a Claude replacement — a smaller fine-tuned model that closes half the gap at $0 marginal cost.
Full generation + dedupe pipeline in text2sql-finetune.
How to use block — a stub prompt makes the model hallucinate tables.Built as a self-study project to demonstrate the full LLM fine-tuning workflow: synthetic dataset generation, deduplication (including test-set leak check), LoRA/PEFT on HuggingFace TRL, and evaluation against an existing multi-agent baseline. The repo README has the "What I learned" section — Claude data redundancy, GPU compatibility, QLoRA-vs-LoRA trade-off.
Apache-2.0. Base model retains its own Qwen2.5 license.
| 9.1s |