Instructions to use LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1",
	filename="ornith-1.0-35b-IQ4_XS-MTP-graft-headQ6.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1 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 LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M

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 LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M

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 LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M

Use Docker

docker model run hf.co/LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M

LM Studio
Jan

vLLM

How to use LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M

Ollama
How to use LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1 with Ollama:
```
ollama run hf.co/LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M
```

Unsloth Studio

How to use LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1 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 LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1 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 LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1 to start chatting

How to use LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M

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": "LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M

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 LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M

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 "LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M" \
  --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 LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1 with Docker Model Runner:
```
docker model run hf.co/LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M
```

Lemonade

How to use LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M

Run and chat with the model

lemonade run user.Ornith-1.0-35B-GGUF-llamacpp-tp1-Q4_K_M

List all available models

lemonade list

MTP Quality

by engrtipusultan - opened 24 days ago

Discussion

engrtipusultan

24 days ago

How is your MTP different from other MTPs lets say

https://huggingface.co/skinnyctax/Ornith-1.0-35B-Q6_K-Frankenstein-MTP-GGUF

Or
https://huggingface.co/wang-yang/Ornith-1.0-35B-MTP-GGUF

Who is reporting 92% acceptance.

LordNeel

Owner 24 days ago

The wang-yang model is not doing a fundamentally different MTP method. It is a single Q6_K GGUF with a same architecture MTP head grafted into Ornith, using llama.cpp draft-mtp. Their card says the base Ornith did not ship MTP heads, then they appended the blk.40.* / nextn.* tensors, patched metadata, and did no retraining.

The 92.2% number is specifically their M3 Max, single-prompt, n_max=1 benchmark: AR 66.6 tok/s, MTP 83.8 tok/s, 92.2% acceptance, 1.26x speedup.
At n_max=2, their acceptance drops to 82.5%; at n_max=4, 68.1% .

Our comparable artifact, ornith-1.0-35b-IQ4_XS-MTP-graft-headQ6.gguf, is structurally the same graft style, but requantized: IQ4_XS body + preserved Q6_K/F16/F32 grafted MTP head.
See ornith-35b-lab/hf_upload/README.md:72.

We also reproduce high acceptance: ~89.67% at n_max=1, and strong n_max=2 positional acceptance.

The real difference is runtime profile, not graft layout. On our batched RTX PRO 6000 server benchmark, the original wang-yang Q6_K-MTP also hit high acceptance, 90.67%, but slowed down: 550.29 tok/s no-MTP vs 257.74 tok/s MTP n_max=1. Our IQ4_XS graft behaves similarly at c16: high acceptance, but no batched throughput win.
See ornith-35b-lab/hf_upload/benchmarks/llamacpp-iq4-xs-mtp-chaincorr-profile.md:46.

Their 92% is real, and ours is not meaningfully different in acceptance when comparing the integrated graft. The remaining issue is llama.cpp batched verification/scheduling on our tp=1 server workload, not draft-head quality.

engrtipusultan

23 days ago

Why your gguf has blk.41 42 and 43. No other GGUF have more than 40

LordNeel

Owner 23 days ago

The base Ornith model has 40 normal transformer blocks: blk.0 through blk.39.

The extra blocks are MTP draft layers, not extra base-model layers.

In llama.cpp GGUF layout, MTP layers are appended after the trunk blocks:

plain/non-MTP GGUF: blk.0 ... blk.39
single-head integrated MTP GGUF: adds blk.40, with nextn_predict_layers=1
4-head experimental MTP draft GGUF: adds blk.40, blk.41, blk.42, blk.43, with nextn_predict_layers=4

I just removed the draft and experimental mtp drafts. What you saw was probably one of the experimental external MTP draft artifacts, like the h2r10prefix drafts. Those were for speculative decoding experiments, not normal target only base models.

The main integrated release artifact ornith-1.0-35b-IQ4_XS-MTP-graft-headQ6.gguf is the single-MTP-layer graft and should only append blk.40.

engrtipusultan changed discussion status to closed 23 days ago

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