mistralai/Mistral-7B-Instruct-v0.3 fine-tuned on text-to-SQL, merged and left in bf16. It is the ceiling arm of a panel of 9 arms: every quantized arm below was made from this checkpoint and allocated the same byte budget, so their accuracies differ by method and not by size.

What this is


base model	mistralai/Mistral-7B-Instruct-v0.3
fine-tune	lora r=32, 2.0 epoch over 39,531 text-to-SQL conversations
the adapter it merged	VikramPal/mistral-7b-instruct-v0.3-lora
training data	`gretelai/synthetic_text_to_sql`, `Salesforce/wikisql`, `b-mc2/sql-create-context`
quantization	none -- this is the bf16 fine-tune every quantized arm was made from
size on disk	13.500 GiB (16.0000 bits per parameter)
loads with	`transformers`

Results

Execution match on 2,454 held-out text-to-SQL problems: the generated query is run against the schema and compared to the reference result set.

arm	exec match	size	bits/param
bf16	78.16%	13.500 GiB	16.0000
gptq_4b	78.28%	3.692 GiB	4.3760
awq_4b	77.91%	3.692 GiB	4.3760
dq_4b	78.08%	3.692 GiB	4.3754
gptq_3b	6.68%	2.858 GiB	3.3869
awq_3b	74.16%	2.858 GiB	3.3869
dq_3b	75.22%	2.857 GiB	3.3859
gptq_3b_asym_noao	76.08%	2.858 GiB	3.3869
gptq_3b_asym	3.99%	2.858 GiB	3.3869

This arm, by evaluation source:

eval source	exec match	items
`gretel`	77.02%	818
`spider`	63.57%	818
`wikisql`	93.89%	818

How this arm compares

McNemar exact over the per-item hits, so every row is a paired test on the same problems in the same order. p (Holm) is step-down corrected within the family the panel declared, not within this card.

comparison	delta (pts)	95% CI	p	p (Holm)	verdict
4b GPTQ vs bf16	+0.12	[-0.54, +0.79]	0.81	1	not separated
4b AWQ vs bf16	-0.24	[-1.05, +0.56]	0.621	1	not separated
4b DynQuant vs bf16	-0.08	[-0.83, +0.67]	0.915	1	not separated
3b GPTQ vs bf16	-71.48	[-73.30, -69.65]	0	0	separated
3b AWQ vs bf16	-3.99	[-5.29, -2.70]	1.8e-09	9.01e-09	separated
3b DynQuant vs bf16	-2.93	[-4.16, -1.71]	3.55e-06	1.42e-05	separated

What is not claimed

The arms above are not all the same scheme. In this panel GPTQ runs symmetric with no activation reordering, asymmetric with no activation reordering, and asymmetric with group activation reordering; AWQ runs asymmetric with no activation reordering. DynQuant's quantizer is asymmetric and does not reorder columns, which is a property of the method rather than a recipe flag, so the panel records no scheme for its arms. Where a comparison above pairs a symmetric arm against an asymmetric one its delta spans two differences at once -- how the bits were allocated, and whether a zero point was stored per group -- so a large gap between those two arms is not on its own evidence about allocation. The comparison that isolates it is in this panel: gptq_3b and gptq_3b_asym_noao are the same method at the same byte anchor and differ in the scheme alone, so the difference between those two rows is the scheme and nothing else.
Storage, measured; throughput, not. The number reported here is bytes on disk and execution match. This card makes no claim about decode speed or peak VRAM against an fp16 baseline, because this panel did not measure either.
One task. Execution match on held-out text-to-SQL is what was scored. It says nothing about how this arm behaves on anything else, and a quantization that holds one task can lose another.

Install

This checkpoint is plain bf16 and loads with transformers alone. It is the ceiling arm of a DynQuant panel: the quantized arms in the table above are this same fine-tune at a fraction of the size, and those need the package.

pip install dynquant

Source, format spec, and the allocator that produced their bit maps: https://github.com/kambojvikram/dynquant

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("VikramPal/mistral-7b-instruct-v0.3-bf16", device_map="cuda")
tokenizer = AutoTokenizer.from_pretrained("VikramPal/mistral-7b-instruct-v0.3-bf16")

Provenance

panel model: /workspace/runs/s4/mistral7b-v03.text2sql/merged
parameters counted: 7,248,023,552
byte target: --
fine-tune: 2472 steps, train loss 0.0540, 3.8 h
fine-tune commit: 5959fe04b7db0512eaf7567d77a43f3df5860651
evaluation: 2,454 problems in 10 min

VikramPal/mistral-7b-instruct-v0.3-text2sql-bf16

Mistral-7B-Instruct-v0.3 text-to-SQL, bf16