metadata
license: apache-2.0
pretty_name: AWQ Quant Quality Evals
tags:
- evaluation
- quantization
- awq
- openllm
- mteb
- qwen
- vllm
task_categories:
- text-generation
- feature-extraction
size_categories:
- n<1K
configs:
- config_name: summary
data_files: summary.jsonl
AWQ Quant Quality Evals
Side-by-side AWQ W4A16 vs BF16 quality measurements for:
| Quant | Base | Suite |
|---|---|---|
| LostGentoo/Qwen3.5-4B-AWQ | Qwen/Qwen3.5-4B | OpenLLM-lite |
| LostGentoo/Qwen3-Embedding-8B-AWQ | Qwen/Qwen3-Embedding-8B | MTEB-lite |
Hardware: NVIDIA RTX 5060 Ti (sm_120, Blackwell).
Files
| File | Contents |
|---|---|
quant_quality_evals.json |
Full combined report |
qwen35_4b_awq_vs_bf16.json |
LLM OpenLLM-lite only |
qwen3_embedding_8b_awq_vs_bf16.json |
Embedding MTEB-lite only |
summary.jsonl |
Flat per-task rows (easy to load) |
Headline results
Qwen3.5-4B (OpenLLM-lite, n=200 MC / task)
| Task | BF16 | AWQ | Recovery |
|---|---|---|---|
| MMLU | 0.710 | 0.700 | 98.6% |
| ARC-Challenge | 0.545 | 0.555 | 101.8% |
| HellaSwag | 0.705 | 0.670 | 95.0% |
| Winogrande | 0.685 | 0.675 | 98.5% |
| TruthfulQA MC1 | 0.295 | 0.285 | 96.6% |
| WikiText-2 PPL | 16.77 | 18.54 | +10.6% |
Mean MC recovery: 98.1%.
GSM8K in this dump is not reliable (short gen budget + weak numeric extraction); treat as a harness caveat, not a quant cliff.
Qwen3-Embedding-8B (MTEB-lite)
| Task | BF16 | AWQ | Recovery |
|---|---|---|---|
| STSBenchmark Spearman | 0.884 | 0.881 | 99.6% |
| SciFact nDCG@10 | 0.787 | 0.777 | 98.8% |
| STS score Pearson AWQ vs BF16 | - | - | 0.996 |
Methodology
- LLM MC tasks: greedy average-logprob scoring of answer continuations (ARC, HellaSwag, Winogrande, MMLU, TruthfulQA MC1), n=200 shuffled with seed 42.
- WikiText-2 PPL: manual NLL from logits, 32 non-empty docs, max length 512.
- Embeddings: last-token pooling + L2 normalize; retrieval queries use Qwen instruct prefix; SciFact nDCG@10 over test qrels.
- Harness: custom script on top of cached HF datasets (lm-eval / mteb package install was unavailable in the run environment).
Load
from datasets import load_dataset
import json
from huggingface_hub import hf_hub_download
rows = load_dataset("LostGentoo/awq-quant-evals", split="train")
print(rows[0])
path = hf_hub_download("LostGentoo/awq-quant-evals", "quant_quality_evals.json", repo_type="dataset")
report = json.load(open(path))
print(report["qwen35_4b"]["recovery_pct"])
License
Apache-2.0. Eval numbers are measurements of third-party base models under their own licenses.