Instructions to use Tribbler/ornith-1.0-apex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tribbler/ornith-1.0-apex with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Tribbler/ornith-1.0-apex",
	filename="ornith-1.0-35b-APEX-Balanced-MTP.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Tribbler/ornith-1.0-apex with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Tribbler/ornith-1.0-apex
# Run inference directly in the terminal:
llama cli -hf Tribbler/ornith-1.0-apex

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Tribbler/ornith-1.0-apex
# Run inference directly in the terminal:
llama cli -hf Tribbler/ornith-1.0-apex

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Tribbler/ornith-1.0-apex
# Run inference directly in the terminal:
./llama-cli -hf Tribbler/ornith-1.0-apex

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Tribbler/ornith-1.0-apex
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Tribbler/ornith-1.0-apex

Use Docker

docker model run hf.co/Tribbler/ornith-1.0-apex

LM Studio
Jan
Ollama
How to use Tribbler/ornith-1.0-apex with Ollama:
```
ollama run hf.co/Tribbler/ornith-1.0-apex
```

Unsloth Studio

How to use Tribbler/ornith-1.0-apex with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Tribbler/ornith-1.0-apex to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Tribbler/ornith-1.0-apex to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Tribbler/ornith-1.0-apex to start chatting

How to use Tribbler/ornith-1.0-apex with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Tribbler/ornith-1.0-apex

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Tribbler/ornith-1.0-apex"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Tribbler/ornith-1.0-apex with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Tribbler/ornith-1.0-apex

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Tribbler/ornith-1.0-apex

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Tribbler/ornith-1.0-apex with Docker Model Runner:
```
docker model run hf.co/Tribbler/ornith-1.0-apex
```

Lemonade

How to use Tribbler/ornith-1.0-apex with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Tribbler/ornith-1.0-apex

Run and chat with the model

lemonade run user.ornith-1.0-apex-{{QUANT_TAG}}

List all available models

lemonade list

Ornith-1.0-35B — APEX GGUF

APEX (Adaptive Precision for EXpert Models) quantizations of Ornith-1.0-35B, an open-source coding MoE model by DeepReinforce (MIT license, based on Qwen 3.5 architecture).

These quants were produced using the apex-quant toolchain. APEX is a MoE-aware mixed-precision quantization strategy that classifies tensors by role (routed expert, shared expert, attention) and applies a layer-wise precision gradient — edge layers get higher precision, middle layers more aggressive compression.

Files

Each profile comes in two variants:

Base — the quantized model standalone
-MTP — includes the bundled MTP (multi-token prediction) head, quantized to Q8_0 (near-lossless), for self-speculative decoding via --spec-type draft-mtp. Requires a recent llama.cpp build with MTP support.

I-variants were calibrated with a diverse importance matrix (chat, code, reasoning, tool-calling, multilingual) for improved downstream accuracy.

File	Profile	Size	Best For
`ornith-1.0-35b-APEX-I-Mini.gguf`	I-Mini	14 GB	Smallest viable, fastest inference
`ornith-1.0-35b-APEX-I-Mini-MTP.gguf`	I-Mini + MTP	14 GB	Smallest viable + self-spec
`ornith-1.0-35b-APEX-Compact.gguf`	Compact	17 GB	Consumer GPUs, general purpose
`ornith-1.0-35b-APEX-Compact-MTP.gguf`	Compact + MTP	17 GB	Consumer GPUs + self-spec
`ornith-1.0-35b-APEX-I-Compact.gguf` 🏆	I-Compact	17 GB	Consumer GPUs, best quality at this size
`ornith-1.0-35b-APEX-I-Compact-MTP.gguf` 🏆	I-Compact + MTP	17 GB	Consumer GPUs, best quality + self-spec
`ornith-1.0-35b-APEX-Quality.gguf`	Quality	22 GB	Highest quality standard
`ornith-1.0-35b-APEX-Quality-MTP.gguf`	Quality + MTP	23 GB	Highest quality + self-spec
`ornith-1.0-35b-APEX-I-Quality.gguf`	I-Quality	22 GB	Highest quality with imatrix
`ornith-1.0-35b-APEX-I-Quality-MTP.gguf`	I-Quality + MTP	23 GB	Highest quality + imatrix + self-spec
`ornith-1.0-35b-APEX-Balanced.gguf`	Balanced	24 GB	General purpose, best trade-off
`ornith-1.0-35b-APEX-Balanced-MTP.gguf`	Balanced + MTP	25 GB	General purpose + self-spec
`ornith-1.0-35b-APEX-I-Balanced.gguf` 🏆	I-Balanced	24 GB	Best overall — lowest KL divergence
`ornith-1.0-35b-APEX-I-Balanced-MTP.gguf` 🏆	I-Balanced + MTP	25 GB	Best overall + self-spec

Profile Precision Breakdown

APEX applies a layer-wise precision gradient to MoE expert weights. I-variants additionally use a diverse imatrix (chat, code, reasoning, tool-calling) that improves downstream accuracy and lowers KL divergence.

Profile	Edge (blk 0-4, 35-39)	Near-Edge (blk 5-9, 30-34)	Middle (blk 10-29)	Shared Expert	Attention
Quality	Q6_K	Q5_K	IQ4_XS	Q8_0	Q6_K
Balanced	Q6_K	Q5_K	Q5_K	Q8_0	Q6_K
Compact	Q4_K	Q3_K	Q3_K	Q6_K	Q4_K
Mini	Q3_K_M	Q3_K_M	IQ2_S	Q4_K	Q3_K_M

Quality and Mini use a 3-tier gradient. Balanced and Compact use a simpler 2-tier gradient (edge vs. middle) — their "Near-Edge" and "Middle" columns are the same precision.

MTP Head

The bundled MTP head (blk.40.* including the nextn.* projection + norms) is quantized to Q8_0 (near-lossless) for high draft accuracy. Enable with:

llama-server -m ornith-1.0-35b-APEX-...-MTP.gguf --spec-type draft-mtp

Usage Examples

llama.cpp server (basic)

llama-server \
  -m ornith-1.0-35b-APEX-I-Compact.gguf \
  -ngl 99 \
  -c 32768 \
  --flash-attn on \
  --temp 0.6 \
  --top-p 0.95

With self-speculative decoding (MTP variants)

llama-server \
  -m ornith-1.0-35b-APEX-I-Compact-MTP.gguf \
  --spec-type draft-mtp \
  -ngl 99 \
  -c 32768 \
  --flash-attn on

llama.cpp server with vision

Ornith has a built-in vision encoder. Vision support in GGUF format is experimental — if a compatible mmproj becomes available, pass it with --mmproj.

Hardware Notes

Profile	Minimum VRAM	Recommended VRAM
I-Mini	16 GB	24 GB
Compact / I-Compact	20 GB	24 GB
Quality / I-Quality	24 GB	32 GB
Balanced / I-Balanced	24 GB (tight)	32 GB+

Acknowledgements

Base model: DeepReinforce — Ornith-1.0-35B (MIT)
APEX quantization: LocalAI team
MTP donor tensors: IHaveNoClueAndIMustPost
Calibration dataset: v3+ultrachat (pile-10k, GSM8K, eaddario imatrix-calibration, HuggingFaceH4/ultrachat_200k)
Built on llama.cpp

Downloads last month: -

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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Model tree for Tribbler/ornith-1.0-apex

Base model

deepreinforce-ai/Ornith-1.0-35B

Quantized

(26)

this model