Giancarmine Salucci
4ad296608b
- 5 models: SmolLM3-3B, Gemma4-E2B/E4B, Qwen3-4B, Qwen3.5-9B - TurboQuant image (FORCE_MMQ): +6-11% free speed on Turing GPUs - Bigctx profiles (-nkvo KV in RAM): 2-16x context gain - turbo2 KV: 2x smaller, benchmarked against PPL quality gate - Per-model env files with justified parameters - kv_quant_test.sh + cpu_ctx_test.sh benchmark scripts - docs/FINDINGS.md: surprises, pitfalls, recommendations - docs/ARCHITECTURE.md: compose + test script design
43 lines
1.3 KiB
Plaintext
43 lines
1.3 KiB
Plaintext
# ==============================================================================
|
|
# Qwen3-4B-Instruct Q4_K_M — Alibaba (May 2025)
|
|
# Architecture: Decoder-only transformer, GQA
|
|
# - 4B params, 32 layers
|
|
# - 32K native context (128K with YaRN)
|
|
# Model size: ~2.4 GB Q4_K_M | Full GPU fit (ngl=99)
|
|
# Features: thinking mode (/think /no_think), tool calling, 119 languages,
|
|
# Apache 2.0. Strong code + reasoning. Best ecosystem (most fine-tunes).
|
|
#
|
|
# Download:
|
|
# huggingface-cli download bartowski/Qwen3-4B-GGUF \
|
|
# Qwen3-4B-Q4_K_M.gguf --local-dir ./models/
|
|
#
|
|
# NOTE: Verify exact filename after download:
|
|
# ls models/Qwen3-4B*
|
|
# ==============================================================================
|
|
|
|
MODEL_FILE=Qwen3-4B-Q4_K_M.gguf
|
|
|
|
# All layers fit — ~2.4 GB leaves ~1.3 GB free for KV + compute
|
|
N_GPU_LAYERS=99
|
|
|
|
# Benchmarked 2026-05-05 on GTX 1650 Ti (3717 MiB):
|
|
# Max ctx=8192 (12K OOM). Full attention — all KV must fit at full ctx.
|
|
# GGUF native limit=40960, but VRAM walls at ~8K.
|
|
# Baseline: 181 pp / 41.6 tg t/s. At 8K ctx fa=1: 191 pp / 44.3 tg t/s (+6%).
|
|
CTX_SIZE=16384
|
|
|
|
THREADS=6
|
|
THREADS_BATCH=6
|
|
|
|
BATCH_SIZE=512
|
|
UBATCH_SIZE=256
|
|
|
|
CACHE_TYPE_K=q4_0
|
|
CACHE_TYPE_V=q4_0
|
|
|
|
# 1 parallel slot — limited VRAM at 8K ctx with 2.4GB model
|
|
PARALLEL=1
|
|
|
|
# fa=1 gives +6% tg speed on full-attention Qwen3
|
|
EXTRA_ARGS=--flash-attn on --mmap
|