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Open-weight model · Text generation

Ornith-1.5-9B-NVFP4

by Ornith ornith-ai/Ornith-1.5-9B-NVFP4

Ornith-1.5-9B-NVFP4 is an open-weight model for text generation from Ornith, released under MIT License. It has 6.7B parameters and a 262,144-token context. At 16-bit it needs about 16.1 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index. It draws 723.3k downloads a month.

Chirp Chirp! We are introducing Ornith-1.5, a major step toward building foundation models through end-to-end self-improvement.

Parameters6.7B
Context262,144
Weights8.8 GB
Licensemit
AccessOpen weights
Monthly Downloads723.3k

Runs On

What it takes to serve Ornith-1.5-9B-NVFP4 (6.7B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

PrecisionWeightsMemory neededCheapest setupPer hourAlso fits
16-bit 13.5 GB 16.1 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 6.7 GB 8.1 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 3.4 GB 4.0 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 9, 2026.

Ornith-1.5-9B-NVFP4 on every accelerator the SAVRN Index prices, at every precision

SAVRN's Notes on Ornith-1.5-9B-NVFP4

One file, 8.8 GB, published 2026-08-26 under MIT: this is a ready-to-serve NVFP4 repack, so the generic precision table — 16.1 GB at 16-bit, 4.0 GB at 4-bit, one MI300X at $1.85 an hour — is a sizing sketch, not the spec. The shipped checkpoint is the 8.8 GB figure, sized to load on a single consumer-class card, and the 262,144-token context is the headline feature.

The name says 9B while the page reads 6.7B parameters; confirm the checkpoint before benchmarking. MIT removes licensing friction entirely, including commercial use. There are no evaluations on file and no stated lineage beyond the Ornith publisher name, so this is a trust-but-verify download: attractive for a cheap long-context box, but run your own evals before trusting it with real traffic.

Model Card

By Ornith, published under mit, revision 155f200d85ad.

Ornith-1.5-9B

Chirp Chirp! We are introducing Ornith-1.5, a major step toward building foundation models through end-to-end self-improvement.

Ornith-1.5 extends Ornith-1.0 (which was developed on top of Qwen3.5 and Gemma4 with additional continued pretraining, mid-training, and post-training) by expanding the self-improvement loop from scaffold and rollout optimization to jointly optimizing task generation, scaffold construction, and solution rollouts. Rather than relying on a fixed set of human-curated tasks and manually designed harnesses, Ornith-1.5 continuously generates new training tasks, discovers effective strategies for solving them, and improves the policy through reinforcement learning. For more details on the task, harness, and rollout reward design, please refer to our blog.

Ornith 1.5 9B

This model card documents Ornith-1.5-9B, the most lightweight member of the Ornith-1.5 family — a 9B dense model designed for efficient single-GPU deployment, and edge-deployable on mobile devices via its quantized Ornith-1.5-9B-Mobile variant.

Benchmarks

Read the full model card (1,429 words)

Configuration

Architecture
Qwen3_5ForConditionalGeneration
Context length (tokens)
262,144
Layers
32
Hidden size
4,096
Feed-forward size
12,288
Attention heads
16
Key/value heads
4
Head dimension
256
Vocabulary size
248,320
Model type
qwen3_5
Quantization
modelopt

Identity and Version

Repository
ornith-ai/Ornith-1.5-9B-NVFP4
Publisher
Ornith
Task
Text generation
Modality
Text
Library
transformers
Parameters
6.7B parameters
Languages
Not stated by the source
Revision
155f200d85ad58464571c77d5e1122ea5d419d7b
First published
2026-08-26
Last updated
2026-08-26

Files and Weights

17 files, 8.8 GB in total. The weights are 1 file totalling 8.8 GB in safetensors.

Weights1 file · 8.8 GB
Configuration6 files · 68.3 KB
Tokenizer4 files · 22.9 MB
Documentation1 file · 26.8 KB
Other3 files · 1.7 MB
Repository2 files · 118.5 KB
Every file
FileTypeSizeSHA-256
model.safetensorsWeights8.8 GB 1cdf915951ce
config.jsonConfiguration38.5 KB —
generation_config.jsonConfiguration206 B —
hf_quant_config.jsonConfiguration27.6 KB —
preprocessor_config.jsonConfiguration390 B —
processor_config.jsonConfiguration1.2 KB —
video_preprocessor_config.jsonConfiguration385 B —
README.mdDocumentation26.8 KB —
assets/ornith_9b_eval.pngOther724.3 KB 5f13eb1febaa
assets/ornith_logo.pngOther962.4 KB 458ee0d85bae
chat_template.jinjaOther7.6 KB —
.gitattributesRepository1.7 KB —
.quant_summary.txtRepository116.8 KB —
merges.txtTokenizer3.4 MB —
tokenizer.jsonTokenizer12.8 MB 5f9e4d4901a9
tokenizer_config.jsonTokenizer16.7 KB —
vocab.jsonTokenizer6.7 MB —

License and Download

License
mit
Access
Open weights, no gate
Download size
8.8 GB
Download from Ornith

Released by Ornith through its official repository on Hugging Face. Read the license.

Memory Requirements

PrecisionWeights in memory
As published8.8 GB
16-bit13.5 GB
8-bit6.7 GB
4-bit3.4 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About Ornith-1.5-9B-NVFP4

How much GPU memory does Ornith-1.5-9B-NVFP4 need?

About 16.1 GB at 16-bit and 4 GB at 4-bit: the weights (6.7B parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run Ornith-1.5-9B-NVFP4 on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

Can I use Ornith-1.5-9B-NVFP4 commercially?

Yes. Ornith-1.5-9B-NVFP4 is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.

What is Ornith-1.5-9B-NVFP4's context length?

262,144 tokens, from the maximum position embeddings in its published configuration.

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