SASVAAI/GLM-4.7-Flash-sql-create-context

GLM-4.7-Flash Text-to-SQL (LoRA)

Given a natural-language question and a CREATE TABLE schema, emits exactly one SQL query answering that question against that schema. For natural-language query interfaces over a known relational schema.

This is a LoRA adapter for zai-org/GLM-4.7-Flash, trained with QLoRA (4-bit NF4 base, bf16 compute) via TRL SFT.

Model details

Trainable parameters: 118,140,928 — 0.3945% of the base. The adapter file is 472,671,000 bytes (752 fp32 tensors: a lora_A + lora_B pair for each of the 8 target modules across all 47 layers).

GLM-4.7-Flash uses Multi-head Latent Attention, so the attention target modules are the MLA projections (q_a_proj/q_b_proj/kv_a_proj_with_mqa/kv_b_proj), not q_proj/k_proj/v_proj. Targeting the conventional names would silently adapt nothing.

Intended use

Direct use. Translate one English question plus one CREATE TABLE schema into one SQL query. The model was trained on a specific prompt shape and that shape is part of the contract:

• —System prompt (verbatim): "You are a text-to-SQL engine. Given a natural-language question and a CREATE TABLE schema, output exactly one SQL query that answers the question against the provided schema. Output only the raw SQL query on a single line with no explanation, no markdown formatting, and no additional text."
• —User turn: the question, a blank line, then the schema inside a fenced code block.
• —Applied through the tokenizer's chat template (chat_template.jinja, shipped in this repo) with enable_thinking=False. Do not concatenate strings by hand.
• —The query is the first line of the generation; discard anything after it.

Out of scope.

• —Not validated against a live database. The model is scored on string similarity to a reference query, never on execution. A syntactically perfect query can still be semantically wrong. Parse and, where you can, dry-run against the real schema before trusting output.
• —Never interpolate output into a privileged connection. Treat generated SQL as untrusted input: run it read-only, with least privilege, on a connection that cannot write or drop.
• —Multi-table joins, CTEs, window functions, subqueries, and DDL/DML are largely out of distribution — the training data is dominated by single-table SELECTs. Measured join accuracy is poor (see Limitations).
• —Dialect is not controllable. The model reproduces the source corpus's conventions (double-quoted string literals, lower-cased comparison values), which are not portable to every engine.
• —Not a general-purpose assistant. It emits a bare query, never prose.

How to get started

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel

BASE = "zai-org/GLM-4.7-Flash"
ADAPTER = "SASVAAI/GLM-4.7-Flash-sql-create-context"

# 4-bit NF4 matches the numerics the adapter was trained against. A bf16 base
# also works and scores the same (see Merged-weights equivalence) but needs
# ~60 GB rather than ~22 GB.
bnb = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

tokenizer = AutoTokenizer.from_pretrained(ADAPTER)
model = AutoModelForCausalLM.from_pretrained(
    BASE, quantization_config=bnb, dtype=torch.bfloat16, device_map="auto"
)
model = PeftModel.from_pretrained(model, ADAPTER)
model.eval()

SYSTEM = (
    "You are a text-to-SQL engine. Given a natural-language question and a "
    "CREATE TABLE schema, output exactly one SQL query that answers the question "
    "against the provided schema. Output only the raw SQL query on a single line "
    "with no explanation, no markdown formatting, and no additional text."
)

question = "Which kingdom has Suin as its capital?"
schema = "CREATE TABLE table_name_65 (name_of_kingdom VARCHAR, capital VARCHAR)"

messages = [
    {"role": "system", "content": SYSTEM},
    {"role": "user", "content": f"{question}\n\n```\n{schema}\n```"},
]
prompt = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True, enable_thinking=False
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

out = model.generate(**inputs, max_new_tokens=128, do_sample=False)
text = tokenizer.decode(out[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True)
print(text.strip().splitlines()[0])
# -> SELECT name_of_kingdom FROM table_name_65 WHERE capital = "suin"

The base model is ~59 GB in bfloat16, or ~22 GB per GPU under 4-bit NF4.

Decoding matters. This model was evaluated with greedy decoding (do_sample=False, max_new_tokens=128). Sampling will not reproduce the reported numbers.

Serving note. These adapter weights do not load as a vLLM LoRA on this architecture — vLLM's MLA path asserts inside DeepSeekV2FusedQkvAProjLinear because q_a_proj and kv_a_proj_with_mqa are fused into one module that a LoRA cannot be attached to. A merged build of these weights serves under vLLM without complaint. For vLLM deployment, merge first (peft.merge_and_unload()).

Training details

Data. A 4,000-pair subset of `b-mc2/sql-create-context` (78,577 pairs, itself derived from WikiSQL and Spider), split 90/10 by this project's data-generation stage. Each record is {"instruction": <question>, "input": <CREATE TABLE ...>, "output": <SQL>}. The subset selection is a generated artifact, not a published split — the id/instruction/input/gold tuples in predictions.jsonl are the authoritative record of what was evaluated.

Method

qlora is one of three methods considered for this model, alongside 8-bit LoRA and attention-only 4-bit LoRA. All three were tried; qlora scored highest every time it was compared against the other two on the same data.

No refinement stage ran; the published weights are the SFT adapter.

Final hyperparameters

Effective batch size: 16 (1 x 4 x 4). Optimizer steps: 675.

KD parameters are omitted deliberately — this is a qlora run, not bf16_lora_kd, so KD_ALPHA/KD_BETA/KD_TEMPERATURE carry inert defaults that would imply distillation that did not happen.

This configuration is not a unique optimum. Other configurations reached the same exact_match; this one was published for being the simplest and cheapest of them — fewest epochs, shortest training time — and because it scored marginally higher on BLEU and ROUGE-L. Treat the values as a good working point, not a tuned maximum.

Observed training metrics.

No eval loss was computed during training; the loop scores on generation, not perplexity. Loss falls from 2.3882 at step 10 to 1.1077 at step 20 and 0.5940 by step 170, then improves slowly to ~0.45 by step 660. The task is essentially learned within the first quarter of epoch 1; epochs 2 and 3 together buy roughly 0.14 of training loss.

Evaluation

Protocol. All 400 validation pairs, no sampling. Predictions generated greedily (do_sample=False, max_new_tokens=128) through the same chat template used in training, against a 4-bit NF4 base to match training numerics. The predicted query is the first line of the generation, stripped. No constrained decoding and no SQL grammar were applied. Exact match is byte equality against the reference query; BLEU and ROUGE-L are computed over the same strings.

Baseline for comparison. Not measured. The untuned zai-org/GLM-4.7-Flash was never scored on this split, so these numbers quantify the fine-tuned model's performance but do not establish how much of it the fine-tuning is responsible for.

This is a validation split, not a held-out test set. Hyperparameters were selected against it, so expect optimistic bias. A clean estimate needs a third split that was never used for selection.

Limitations and bias

Exact match understates the model; BLEU and ROUGE-L overstate it. The 0.805 / 0.940 / 0.986 spread is the story of this model. Exact match is byte equality, so a semantically identical query loses the point on quoting or a missing DISTINCT:

question: Find the states where have some college students in tryout and their decisions are yes.
schema:   CREATE TABLE tryout (cName VARCHAR, decision VARCHAR);
          CREATE TABLE college (state VARCHAR, cName VARCHAR)
gold:     SELECT DISTINCT T1.state FROM college AS T1 JOIN tryout AS T2 ON T1.cName = T2.cName WHERE T2.decision = 'yes'
pred:     SELECT T1.state FROM college AS T1 JOIN tryout AS T2 ON T1.cName = T2.cName WHERE T2.decision = "yes"

Relaxing to case- and whitespace-insensitive comparison moves exact match from 0.805 to 0.8125 (325/400) — so only 3 of the 78 misses are pure formatting. The other 75 are real semantic or structural errors. ROUGE-L at 0.986 mostly measures that both strings are short SQL over the same table names; it is not evidence of correctness.

Output is never degenerate. All 400 golds and all 400 predictions begin with SELECT; the model never emitted prose, markdown, or an empty string. Format compliance is not the failure mode.

Joins are the failure mode. Miss rate by gold-query feature:

The model is reliable on the shape it saw constantly (one table, one predicate) and unreliable on the shape it barely saw. Do not deploy this on a multi-table schema. Note the join, ORDER BY, and GROUP BY rows rest on 13, 3, and 8 examples respectively — read them as a strong warning, not a precise rate.

Complexity tracks length: correct predictions have a mean gold length of 10.6 tokens, misses 12.8.

Dialect is baked in. The model emits the source corpus's double-quoted string literals and lower-cases comparison values. On engines where "x" is an identifier rather than a string (PostgreSQL, ANSI mode), output will not run unmodified.

No execution or injection safety. Correctness is measured only as string similarity to a reference. Nothing here prevents a generated query from being expensive, wrong, or destructive against a real database.

Inherits all biases and limitations of the base model. This adapter changes 0.3945% of the parameters and was not evaluated for social bias, safety, or fairness.

Merged-weights equivalence

A merged build of these weights (base + adapter folded into one standalone bf16 model, W + (alpha/r) * B @ A) was evaluated on the identical split:

39 of 400 predictions differ (9.75%) — far more churn than a bf16-trained adapter would show, because merging into an unquantised base genuinely changes the numerics the adapter was fitted against. The changes cancel exactly: 10 predictions flip correct→incorrect and 10 flip incorrect→correct, leaving exact match identical and BLEU/ROUGE-L marginally higher.

So a merged distribution is behaviourally equivalent in aggregate but not prediction-for-prediction. Merging is also the practical route to vLLM serving (see Serving note). MIT permits distributing derivative works, so publishing a merged build is allowed.

Environmental impact

Covers the training of these published weights only. It excludes the wider hyperparameter search that selected them, which cost substantially more.

Framework versions

• —PEFT 0.18.1
• —TRL: 1.0.0
• —Transformers: 5.7.0.dev0
• —Pytorch: 2.5.1+cu121
• —Datasets: 4.8.4
• —Tokenizers: 0.22.2
• —bitsandbytes: 0.49.2

transformers is a git-main build: GLM-4.7-Flash's Glm4MoeLite architecture is not in the stable PyPI release.

Licence

Adapter weights: MIT, inherited from `zai-org/GLM-4.7-Flash` (verified via the Hub API). Training data: `b-mc2/sql-create-context`, licensed CC-BY-4.0 — downstream use should carry that attribution.

Citation

@misc{glm47flash_sql_create_context_lora_2026,
  title  = {GLM-4.7-Flash Text-to-SQL (LoRA)},
  author = {{SASVA AI Model Cognition Labs (MCL) Team}},
  year   = {2026},
  url    = {https://huggingface.co/SASVAAI/GLM-4.7-Flash-sql-create-context}
}