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

Ornith-1.0-35B-GGUF-llamacpp-tp1 / benchmarks /kld-quant-vs-bf16-top64.md

LordNeel

Update trained MTP docs and serving wrappers

538357c verified 25 days ago

preview code

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Ornith-1.0-35B GGUF Quant KLD Probe

Date: 2026-06-27

Hardware: single RTX PRO 6000 Blackwell Max-Q 96GB, tp=1.

Backend: local llama.cpp CUDA server from vendor/llama.cpp/build-cuda/bin/llama-server.

Method

This corrected probe compares each quant against the BF16 GGUF baseline using approximate KL(P_bf16 || P_quant) over next-token top-64 distributions.

Prompts: first 32 rows of data/eval/coding_sft_eval_messages.jsonl, rendered with the Ornith chat template, final assistant answer removed, and generation prompt added.

API path: native llama.cpp /completion with n_predict=1, temperature=-1, n_probs=64, and cache_prompt=false.

Matching: distributions are keyed by token ID, not token text.

Approximation: missing candidate top-K tokens receive candidate_tail_mass / (vocab_size - K), and one explicit tail bucket is included. In this run, the BF16 top-64 distribution covered mean probability mass 0.999965, so the top-64 approximation captured essentially all next-token mass for these prompts. This is still a fast next-token quant-regression signal, not a full-vocab or full-sequence evaluation.

BF16 baseline loaded VRAM: 66,118 MiB / 64.57 GiB.

Results

Quant	GGUF GiB	KLD profile VRAM GiB	Mean KLD nats	Mean KLD bits	P50 nats	P95 nats	Max nats	Top-1 match
Q3_K_M	15.61	16.32	0.3620	0.5223	0.2548	1.1077	1.3730	27/32 (84.4%)
IQ4_XS	17.64	19.34	0.1426	0.2057	0.0868	0.3195	0.6586	27/32 (84.4%)
Q4_K_M	19.71	20.35	0.0864	0.1247	0.0379	0.2877	0.4503	29/32 (90.6%)
Q5_K_M	23.03	23.61	0.0354	0.0510	0.0235	0.0943	0.2497	30/32 (93.8%)
Q6_K	26.56	27.07	0.0165	0.0238	0.0092	0.0513	0.0586	32/32 (100.0%)
Q8_0	34.37	34.77	0.0108	0.0156	0.0052	0.0440	0.0590	31/32 (96.9%)

Interpretation

Q8_0 has the lowest mean and median KLD in this corrected top-64 probe. Q6_K is nearly as close and has perfect top-1 agreement across all 32 prompts, making it the best quality/footprint tradeoff among the higher-precision quants here.

Q5_K_M is a strong middle option: much lower drift than Q4_K_M and Q3_K_M while still staying materially smaller than Q6_K and Q8_0. IQ4_XS sits between Q3_K_M and Q4_K_M: materially lower KLD than Q3_K_M at a smaller footprint than Q4_K_M, but not as close to BF16 as Q4_K_M. Q4_K_M remains the speed-focused serving recommendation from throughput benchmarks, but it drifts more than Q5/Q6/Q8. Q3_K_M is the lowest-footprint option and passed behavior checks, but it has the largest KLD drift versus BF16 in this probe.

Raw Artifacts

runs/kld-native-bf16-top64-32prompts.json
runs/kld-native-q3-k-m-top64-32prompts.json
runs/kld-native-q3-k-m-vs-bf16.json
runs/kld-native-iq4-xs-top64-32prompts.json
runs/kld-native-iq4-xs-vs-bf16.json
runs/kld-native-q4-k-m-top64-32prompts.json
runs/kld-native-q4-k-m-vs-bf16.json
runs/kld-native-q5-k-m-top64-32prompts.json
runs/kld-native-q5-k-m-vs-bf16.json
runs/kld-native-q6-k-top64-32prompts.json
runs/kld-native-q6-k-vs-bf16.json
runs/kld-native-q8-0-top64-32prompts.json
runs/kld-native-q8-0-vs-bf16.json
runs/kld-native-vram.csv