Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF

Instructions to use AninoDevel/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use AninoDevel/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF 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 AninoDevel/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf AninoDevel/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AninoDevel/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf AninoDevel/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF:BF16

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 AninoDevel/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf AninoDevel/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF:BF16

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 AninoDevel/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AninoDevel/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF:BF16

Use Docker

docker model run hf.co/AninoDevel/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF:BF16

LM Studio
Jan

vLLM

How to use AninoDevel/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AninoDevel/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AninoDevel/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AninoDevel/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF:BF16

Ollama
How to use AninoDevel/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF with Ollama:
```
ollama run hf.co/AninoDevel/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF:BF16
```

Unsloth Studio

How to use AninoDevel/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF 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 AninoDevel/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF 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 AninoDevel/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AninoDevel/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF to start chatting

How to use AninoDevel/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AninoDevel/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF:BF16

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": "AninoDevel/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use AninoDevel/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AninoDevel/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF:BF16

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 AninoDevel/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF:BF16

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use AninoDevel/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AninoDevel/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF:BF16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "AninoDevel/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF:BF16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use AninoDevel/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF with Docker Model Runner:
```
docker model run hf.co/AninoDevel/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF:BF16
```

Lemonade

How to use AninoDevel/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AninoDevel/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF:BF16

Run and chat with the model

lemonade run user.Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF-BF16

List all available models

lemonade list

Ornith-1.0-35B-AEON-Ultimate-Uncensored MTP GGUF

GGUF conversions of AEON-7/Ornith-1.0-35B-AEON-Ultimate-Uncensored with MTP (Multi-Token Prediction) tensors grafted in for speculative decoding support.

The base model is the AEON abliterated variant of deepreinforce-ai/Ornith-1.0-35B. The MTP tensors are from unsloth/Qwen3.6-35B-A3B-MTP-GGUF.

Available Quantizations

File	Quant	Size	Source Weights	MTP Source
`Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-BF16.gguf`	BF16	71.07 GB (66.19 GiB)	AEON-7/...-BF16	unsloth/Qwen3.6-35B-A3B-MTP-GGUF
`Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-NVFP4.gguf`	NVFP4	23.40 GB (21.80 GiB)	AEON-7/...-NVFP4	unsloth/Qwen3.6-35B-A3B-MTP-GGUF via s-batman
`Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-Q8_0.gguf`	Q8_0	37.80 GB (35.21 GiB)	Quantized from the BF16 GGUF	unsloth/Qwen3.6-35B-A3B-MTP-GGUF

Checksums

a13df4cce8a32b2065d8aea51dcc80d7056fea6c3277266d9b040923a2641840  Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-BF16.gguf
d78f62f6c112de9721390ce8f75b22cf753b3766a640257cc13ca85f16030292  Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-NVFP4.gguf
16696fb2e19b5b3faa316b198524be3dff3652555c67c3f3ea11e811147b219a  Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-Q8_0.gguf

Provenance

Base model

deepreinforce-ai/Ornith-1.0-35B
  -> AEON-7/Ornith-1.0-35B-AEON-Ultimate-Uncensored-BF16
      -> AEON-7/Ornith-1.0-35B-AEON-Ultimate-Uncensored-NVFP4

MTP tensors

All MTP prediction heads originate from unsloth/Qwen3.6-35B-A3B-MTP-GGUF. These are compatible at the tensor-shape level because Ornith-1.0-35B uses the Qwen3.5 MoE architecture and tokenizer family.

BF16: blk.40.* MTP tensors grafted directly from Unsloth's BF16 split GGUF.
NVFP4: blk.40.* MTP tensors grafted via s-batman/Ornith-1.0-35B-NVFP4-MTP-GGUF. Byte-level verification confirms this block is identical to Unsloth's Qwen3.6-35B-A3B-MXFP4_MOE.gguf MTP block: 20 tensors, 512,079,872 tensor payload bytes, combined tensor-name-plus-payload SHA-256 8b8ba06cf776d2cdbf4d4db6714cf69b8a455105fc848bc02c4e5acb62f585f1.
Q8_0: blk.40.* MTP tensors grafted directly from Unsloth's Q8_0 GGUF.

Credit for the Qwen3.6 MTP tensors goes to Unsloth and the original Qwen release. s-batman is acknowledged as the intermediary who performed the NVFP4 graft used here as the practical donor for the NVFP4 file.

Usage

MTP requires a llama.cpp build with draft-mtp speculative decoding support.

llama-cli

llama-cli \
  -m Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-Q8_0.gguf \
  --spec-type draft-mtp \
  --spec-draft-n-max 3 \
  -p "Explain gradient descent in 3 sentences."

llama-server

llama-server \
  -m Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-NVFP4.gguf \
  --host 0.0.0.0 --port 8080 \
  -ngl all \
  -c 65536 \
  --spec-type draft-mtp \
  --spec-draft-n-max 3

Some frontends expose this as "MTP" or "speculative decoding" rather than the raw llama.cpp --spec-type draft-mtp flag.

LM Studio

Download the desired quant file. The model should appear as:

mrexodia/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF:BF16
mrexodia/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF:NVFP4
mrexodia/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF:Q8_0

Enable MTP/speculative decoding in advanced settings if your frontend supports it.

Notes on correctness

MTP is used as a speculative draft. The target model verifies proposed tokens, so a poorly matched MTP head should reduce acceptance rate or speedup rather than change the final verified output distribution. The graft is still experimental and should be benchmarked for your workload.

NVFP4 is intended for hardware and software stacks with NVFP4 support. On unsupported hardware, use the BF16 or Q8_0 files.

Exact production steps

All commands below were run from a llama.cpp checkout with a CUDA build available. Local cache paths are omitted for readability and shown as HuggingFace repo names.

1. Convert AEON BF16 safetensors to body-only BF16 GGUF

The source config advertises MTP, but the AEON BF16 safetensors snapshot does not contain MTP tensors. The body conversion was therefore done with --no-mtp.

python convert_hf_to_gguf.py \
  AEON-7/Ornith-1.0-35B-AEON-Ultimate-Uncensored-BF16 \
  --outtype bf16 \
  --no-mtp \
  --outfile Ornith-1.0-35B-AEON-Ultimate-Uncensored-BF16.gguf

2. Graft BF16 MTP tensors

Copied all 20 donor tensors with prefix blk.40. from:

unsloth/Qwen3.6-35B-A3B-MTP-GGUF/BF16/Qwen3.6-35B-A3B-BF16-00002-of-00002.gguf

into the BF16 body GGUF, then updated:

qwen35moe.block_count = 41
qwen35moe.nextn_predict_layers = 1

The final published filename is:

Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-BF16.gguf

3. Convert AEON NVFP4 safetensors to body-only NVFP4 GGUF

python convert_hf_to_gguf.py \
  AEON-7/Ornith-1.0-35B-AEON-Ultimate-Uncensored-NVFP4 \
  --outtype bf16 \
  --no-mtp \
  --outfile Ornith-1.0-35B-AEON-Ultimate-Uncensored-NVFP4.gguf

The converter detected and preserved the source NVFP4 quantization. --outtype bf16 only affects non-NVFP4 tensors that remain floating point.

4. Graft NVFP4/MXFP4_MOE MTP tensors

Copied all 20 donor tensors with prefix blk.40. from:

s-batman/Ornith-1.0-35B-NVFP4-MTP-GGUF/ornith-1.0-35b-NVFP4_MOE-MTP.gguf

This block was verified byte-for-byte identical to the MTP block in:

unsloth/Qwen3.6-35B-A3B-MTP-GGUF/Qwen3.6-35B-A3B-MXFP4_MOE.gguf

Then updated:

qwen35moe.block_count = 41
qwen35moe.nextn_predict_layers = 1

The final published filename is:

Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-NVFP4.gguf

5. Quantize BF16 body to Q8_0

The Q8_0 trunk was quantized from the body-only BF16 GGUF:

llama-quantize \
  Ornith-1.0-35B-AEON-Ultimate-Uncensored-BF16.gguf \
  Ornith-1.0-35B-AEON-Ultimate-Uncensored-Q8_0-body.gguf \
  q8_0

6. Graft Q8_0 MTP tensors

Copied all 20 donor tensors with prefix blk.40. from:

unsloth/Qwen3.6-35B-A3B-MTP-GGUF/Qwen3.6-35B-A3B-Q8_0.gguf

Then updated:

qwen35moe.block_count = 41
qwen35moe.nextn_predict_layers = 1

The final published filename is:

Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-Q8_0.gguf

7. Metadata updates

Each final GGUF was rewritten with file-specific metadata:

general.name
general.author = mrexodia
general.quantized_by = mrexodia
general.license = mit
general.license.name = MIT License
general.license.link = https://huggingface.co/deepreinforce-ai/Ornith-1.0-35B/blob/main/LICENSE
general.source.huggingface.repository
general.description
general.base_model.count = 2
general.base_model.0.* for the AEON source
general.base_model.1.* for the Unsloth MTP donor
general.tags

The legacy custom key general.base_model was removed in favor of the interoperable general.base_model.{id}.name mapping used by HuggingFace GGUF metadata.

8. Verification

For each final GGUF:

qwen35moe.block_count = 41
qwen35moe.nextn_predict_layers = 1
20 tensors with prefix blk.40. are present
4 tensors under blk.40.nextn.* are present
llama.cpp loaded the model with --spec-type draft-mtp
Smoke test prompt What is 2+2? Answer with just the number. produced 4

License

MIT, inherited from the base model.

Downloads last month: 559

GGUF

Model size

36B params

Architecture

qwen35moe

Hardware compatibility

4-bit

8-bit

16-bit

Model tree for AninoDevel/Ornith-1.0-35B-AEON-Ultimate-Uncensored-MTP-GGUF

Base model

deepreinforce-ai/Ornith-1.0-35B

Finetuned

AEON-7/Ornith-1.0-35B-AEON-Ultimate-Uncensored-BF16

Quantized

(7)

this model