Initial commit: tuned multi-model llama.cpp stack

- 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

envs/.env.qwen3-4b

@@ -0,0 +1,42 @@
+# ==============================================================================
+# 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