DeepReinforce's Ornith-1.0-9B (9B dense, Qwen3.5 family) with YaRN RoPE scaling baked into the GGUF metadata for a 1,048,576-token context window, 4x the native 262,144, shipping with the MTP speculative-decoding layer baked in and a vision tower alongside. Weights bit-identical to the source builds; llama.cpp and Ollama apply the context extension with no extra flags.
Files: MTP-first
File
Size
Pick it when
ornith-1.0-9b-1M-MTP-Q4_K_M.gguf
5.8 GB
Default. MTP layer baked in: +15 to 38 percent decode via llama.cpp speculative decoding
ornith-1.0-9b-1M-MTP-Q8_0.gguf
9.8 GB
Max quality. This is the config that scored 10/10 at the full 1M with f16 KV
mmproj-ornith-9b-f16.gguf
918 MB
Vision, attach with --mmproj
This repo intentionally carries only the MTP builds. The full 10-quant ladder (IQ2_M through bf16) lives on the ModelScope mirror.
Every file, every mirror
Nothing was discontinued: every quant is one click away. Hugging Face carries the curated picks, ModelScope always carries everything, and Ollama serves ready-to-run tags.
On Ollama every tag ships with the vision tower bundled and the 1M rope metadata baked in.
Max quality: Q8_0 weights + f16 KV (M3 Max 128 GB)
10/10
10/10
Same model, same 1M bake. The 1M misses in the budget config are quantization tax, not a context-extension failure; the extension itself is clean, as the max-quality run proves. Prefill time at 1M on M3 Max: about 6.8 hours (prefill_time_9b.png).
MTP
The Qwen3.5 family ships a multi-token-prediction layer in official checkpoints; Ornith's GGUFs dropped it and protoLabsAI restored it. Both files above carry it baked in. Measured decode gains on RTX 5090: 218 tok/s baseline to 250 to 302 tok/s depending on workload (best on code). Output is identical to standard decoding; the trunk verifies every drafted token.
Ollama (1M and vision work; no speculative decoding in Ollama yet):
FROM ./ornith-1.0-9b-1M-MTP-Q4_K_M.gguf
RENDERER qwen3.5
PARSER qwen3.5
PARAMETER num_ctx 262144
How this was built
1M context: YaRN rope-scaling metadata (factor 4.0 over native 262,144) baked into the GGUF header with gguf-py; no weight changes, no fine-tuning. MTP: community GGUF carrying the official layer, vetted and re-baked. Certification: multi-needle harness (in this repo as niah_test.py), f16 KV for certification runs, quantized KV only ever as a labeled budget config. Method and tooling: github.com/satindergrewal/aviary-1m.
Repo history note: the commit history was squashed (July 2026) to reclaim storage from deleted large files, since Hugging Face bills every revision including deleted ones. The real build history of this repo is longer than the log shows. Method, tooling, and full records: aviary-1m.
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