LiquidAI/LFM2.5-8B-A1B fine-tuned on text-to-SQL and quantized to 4 bits with DynQuant. It is one of 7 arms in a panel where every quantized arm was allocated the same byte budget, so the accuracies below differ by method and not by size.

What this is


base model	LiquidAI/LFM2.5-8B-A1B
fine-tune	lora r=32, 1.0 epoch over 49,905 text-to-SQL conversations
training data	`gretelai/synthetic_text_to_sql`, `Salesforce/wikisql`, `b-mc2/sql-create-context`
quantization	DynQuant, 4-bit, per-module widths from a DynQuant allocation
size on disk	4.096 GiB (4.1547 bits per parameter)
loads with	`transformers` with `dynquant` installed

Results

Execution match on 12,000 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	84.29%	15.773 GiB	16.0000
gptq_4b	82.07%	4.097 GiB	4.1565
awq_4b	81.77%	4.097 GiB	4.1565
dq_4b	82.84%	4.096 GiB	4.1547
gptq_3b	60.76%	3.104 GiB	3.1488
awq_3b	57.92%	3.104 GiB	3.1488
dq_3b	79.89%	3.103 GiB	3.1475

This arm, by evaluation source:

eval source	exec match	items
`gretel`	69.67%	3,063
`wikisql`	87.36%	8,937

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 DynQuant vs GPTQ	+0.78	[+0.29, +1.26]	0.00177	0.00353	separated
4b DynQuant vs AWQ	+1.08	[+0.60, +1.55]	1.1e-05	3.31e-05	separated
4b DynQuant vs bf16	-1.45	[-1.84, -1.06]	2.09e-13	2.09e-13	separated

What is not claimed

The accuracy above was measured in bf16, not from this directory. A DynQuant arm is scored by encoding its allocated widths back into bf16 -- the same encoder, the same widths, the same values -- because 91.5% of this model's parameters are batched expert banks and the scoring path applies widths in memory rather than writing a 17 GB decoded copy per arm. The directory you are downloading holds those same values packed. What is carried across from the measurement is the arithmetic; what is not is a claim that the packed and encoded containers were separately scored.
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 directory is packed, so transformers alone cannot open it -- it needs DynQuant's HfQuantizer, which the package registers. Prebuilt CUDA kernels come with it where a wheel exists for your platform, and it falls back to a pure-torch path where one does not.

pip install dynquant

Source, format spec, and the allocator that produced this arm's bit map: https://github.com/kambojvikram/dynquant

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

import dynquant

dynquant.register_hf_quantizer()

model = AutoModelForCausalLM.from_pretrained("VikramPal/LFM2.5-8B-A1B-DynQuant-4bit", device_map="cuda")
tokenizer = AutoTokenizer.from_pretrained("VikramPal/LFM2.5-8B-A1B-DynQuant-4bit")

Provenance

panel model: /workspace/runs/s4/lfm25-8b-a1b.text2sql/merged
parameters counted: 8,467,856,128
byte target this arm was allocated against: 4,399,629,312 B
fine-tune: 1560 steps, train loss 0.1114, 6.7 h
fine-tune commit: d0d33f3bce6f3f59359ce704b16040c7e9ba78f5
evaluation: 12,000 problems in 47 min

VikramPal/LFM2.5-8B-A1B-text2sql-DynQuant-4bit

LFM2.5-8B-A1B text-to-SQL, DynQuant 4-bit