AtomicChat/Ornith-9B-GGUF

text-generationggufUpstream checksumsggufatomic-chatornithdeepreinforce-aillama.cppquantized

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Cataloged from AtomicChat/Ornith-9B-GGUF

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Base model: deepreinforce-ai/Ornith-1.0-9B

Ornith 1.0 9B, self-quantized to GGUF by Atomic Chat. Built straight from DeepReinforce's original weights with a per-tensor importance matrix, so this is not a repack of somebody else's files. Runs fully offline.

Highlights

0.0B parameters: the weights this repo quantizes.
Context length: 262,144 tokens (256K), as published by DeepReinforce.
32 layers: Dense decoder.
Modalities: the base model handles Text, Image; this repo ships text-only quants, it carries no vision projector.
Full imatrix ladder: every quant is calibrated with an importance matrix.
State-of-the-Art Coding Agents: Available in 9B-Dense, 31B-Dense, 35B-MoE, and 397B-MoE (post-trained on top of Gemma 4 and Qwen 3.5), achieving state-of-the-art performance among open-source models of comparable size on coding benchmarks such as Terminal-Bench 2.1, SWE-Bench, NL2Repo and OpenClaw.
Self-Improving Training Framework: Ornith-1.0 employs RL to learn to generate not only solution rollouts, but also the scallfold that drive those rollouts. By jointly optimizing the scaffold and the resulting solution, the model discovers better search trajectories and generates higher-quality solutions.

[!NOTE] These GGUFs are self-quantized from the original weights, not a repack. The importance matrix keeps low-bit quants closer to the full-precision model.

[!IMPORTANT] Always pass --jinja so the Ornith 1.0 9B chat template is applied. Without it the model can emit malformed turns.

Model Overview

Property	Value
Base model	`deepreinforce-ai/Ornith-1.0-9B`
Parameters	0.0B
Layers	32
Context length	262,144 tokens (256K)
Vocabulary	248,320
Modalities	Text, Image in the base model; text only in this repo, it ships no vision projector
Architecture	Dense decoder, 16 attention heads over 4 KV heads, `Qwen3_5ForConditionalGeneration`
This repo	GGUF quants (imatrix). Quants: `IQ4_XS`, `Q4_K_M`, `UD-Q4_K_XL`, `Q5_K_M`, `Q6_K`, `Q8_0`

Scores are DeepReinforce's published results for the base deepreinforce-ai/Ornith-1.0-9B, not our own measurements. Quantization preserves the large majority of this; Q4_K_M and up stay close to full precision.

Choosing a quant

Quant	Size	Notes
`IQ4_XS`	5.2 GB	Excellent quality for size. Recommended low-bit.
`Q4_K_M`	5.6 GB	Recommended default. Best balance of size, speed and quality.
`UD-Q4_K_XL`	6.4 GB	Dynamic. Embeddings and output kept at Q8_0 for higher quality at a Q4 footprint.
`Q5_K_M`	6.5 GB	Higher quality, low loss.
`Q6_K`	7.4 GB	Near lossless, noticeably lighter than Q8_0.
`Q8_0`	9.5 GB	Effectively lossless, reference quality.

[!TIP] Pick the largest file that fits your (V)RAM with room for context. Q4_K_M or UD-Q4_K_XL is the sweet spot for most setups; Q6_K or Q8_0 for maximum fidelity.

Get started

Run Ornith 1.0 9B locally with:

Atomic Chat: the easiest path. Open the app, search AtomicChat/ornith-9b-GGUF, pick a quant, hit Use this model.
llama.cpp: llama-server -hf AtomicChat/ornith-9b-GGUF:Q4_K_M --jinja -c 8192
Ollama: ollama run hf.co/AtomicChat/ornith-9b-GGUF:Q4_K_M
LM Studio / Jan: search the repo id, download any quant.

Best practices

Parameter	Value
temperature	1.0
top_p	1.0
top_k	20

DeepReinforce's recommended sampling configuration for deepreinforce-ai/Ornith-1.0-9B.

Run in llama.cpp

bash

git clone https://github.com/ggml-org/llama.cppcmake llama.cpp -B llama.cpp/build -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ONcmake --build llama.cpp/build --config Release -j --target llama-cli llama-server

How these were made

Download deepreinforce-ai/Ornith-1.0-9B (original weights).
Convert to f16 GGUF with llama.cpp.
Build an importance matrix over our calibration corpus.
Quantize the ladder with --imatrix.
UD-Q4_K_XL additionally pins the token-embedding and output tensors to Q8_0.

License

Original model by DeepReinforce, released under the MIT license. Full terms: MIT. Quantized by Atomic Chat.

Highlights

0.0B parameters: the weights this repo quantizes.

Context length: 262,144 tokens (256K), as published by DeepReinforce.

32 layers: Dense decoder.

Modalities: the base model handles Text, Image; this repo ships text-only quants, it carries no vision projector.

Full imatrix ladder: every quant is calibrated with an importance matrix.

State-of-the-Art Coding Agents: Available in 9B-Dense, 31B-Dense, 35B-MoE, and 397B-MoE (post-trained on top of Gemma 4 and Qwen 3.5), achieving state-of-the-art performance among open-source models of comparable size on coding benchmarks such as Terminal-Bench 2.1, SWE-Bench, NL2Repo and OpenClaw.

Self-Improving Training Framework: Ornith-1.0 employs RL to learn to generate not only solution rollouts, but also the scallfold that drive those rollouts. By jointly optimizing the scaffold and the resulting solution, the model discovers better search trajectories and generates higher-quality solutions.

[!NOTE] These GGUFs are self-quantized from the original weights, not a repack. The importance matrix keeps low-bit quants closer to the full-precision model.

[!IMPORTANT] Always pass --jinja so the Ornith 1.0 9B chat template is applied. Without it the model can emit malformed turns.

Model Overview

Choosing a quant

[!TIP] Pick the largest file that fits your (V)RAM with room for context. Q4_K_M or UD-Q4_K_XL is the sweet spot for most setups; Q6_K or Q8_0 for maximum fidelity.

Get started

Run Ornith 1.0 9B locally with:

Atomic Chat: the easiest path. Open the app, search AtomicChat/ornith-9b-GGUF, pick a quant, hit Use this model.

llama.cpp: llama-server -hf AtomicChat/ornith-9b-GGUF:Q4_K_M --jinja -c 8192

Ollama: ollama run hf.co/AtomicChat/ornith-9b-GGUF:Q4_K_M

LM Studio / Jan: search the repo id, download any quant.

Run in llama.cpp