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
44 lines
1.6 KiB
Plaintext
44 lines
1.6 KiB
Plaintext
# ==============================================================================
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# Gemma 4 E2B-it Q4_K_M — Google DeepMind (April 2025)
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# Architecture: Dense transformer + Per-Layer Embeddings (PLE)
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# - 2.3B effective params (5.1B total with PLE embedding tables)
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# - 35 layers, hybrid local (512-token window) + global attention
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# - 128K context window
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# Model size: ~2.9 GB Q4_K_M | Full GPU fit (ngl=99, VRAM ~3.4 GB total)
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# Modalities: text + image + audio (ASR/translation) + video frames
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#
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# Download:
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# huggingface-cli download bartowski/google_gemma-4-E2B-it-GGUF \
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# google_gemma-4-E2B-it-Q4_K_M.gguf --local-dir ./models/
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#
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# NOTE: Verify the exact filename after download — bartowski naming may vary.
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# Check: ls models/google_gemma*
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# ==============================================================================
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MODEL_FILE=google_gemma-4-E2B-it-Q4_K_M.gguf
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# All 35 layers fit in VRAM. PLE layers are small compute, large embedding lookup.
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N_GPU_LAYERS=99
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# Benchmarked 2026-05-05 on GTX 1650 Ti (3717 MiB):
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# Hybrid sliding-window attention (512-token) keeps KV tiny → 32K ctx fits!
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# 65K/131K OOM (full global-attn layers eat VRAM at large ctx).
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# Baseline: 350 pp / 64.6 tg t/s | At 32K ctx: 365 pp / 66.8 tg t/s (fa=1)
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CTX_SIZE=24576
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THREADS=6
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THREADS_BATCH=6
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BATCH_SIZE=512
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UBATCH_SIZE=256
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# f16 KV — model small, KV overhead negligible even at 32K
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CACHE_TYPE_K=f16
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CACHE_TYPE_V=f16
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# 2 parallel slots — fast model (66 tg t/s), VRAM headroom available
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PARALLEL=2
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# fa=1 confirmed working on hybrid Gemma4 attention (+5% vs fa=0)
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EXTRA_ARGS=--flash-attn on --mmap
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