Instructions to use khudgins/Ornith-1.0-35B-ThinkingCap-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 khudgins/Ornith-1.0-35B-ThinkingCap-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 khudgins/Ornith-1.0-35B-ThinkingCap-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf khudgins/Ornith-1.0-35B-ThinkingCap-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf khudgins/Ornith-1.0-35B-ThinkingCap-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf khudgins/Ornith-1.0-35B-ThinkingCap-GGUF:Q8_0

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 khudgins/Ornith-1.0-35B-ThinkingCap-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf khudgins/Ornith-1.0-35B-ThinkingCap-GGUF:Q8_0

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 khudgins/Ornith-1.0-35B-ThinkingCap-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf khudgins/Ornith-1.0-35B-ThinkingCap-GGUF:Q8_0

Use Docker

docker model run hf.co/khudgins/Ornith-1.0-35B-ThinkingCap-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use khudgins/Ornith-1.0-35B-ThinkingCap-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "khudgins/Ornith-1.0-35B-ThinkingCap-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": "khudgins/Ornith-1.0-35B-ThinkingCap-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/khudgins/Ornith-1.0-35B-ThinkingCap-GGUF:Q8_0

Ollama
How to use khudgins/Ornith-1.0-35B-ThinkingCap-GGUF with Ollama:
```
ollama run hf.co/khudgins/Ornith-1.0-35B-ThinkingCap-GGUF:Q8_0
```

Unsloth Studio

How to use khudgins/Ornith-1.0-35B-ThinkingCap-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 khudgins/Ornith-1.0-35B-ThinkingCap-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 khudgins/Ornith-1.0-35B-ThinkingCap-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use khudgins/Ornith-1.0-35B-ThinkingCap-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf khudgins/Ornith-1.0-35B-ThinkingCap-GGUF:Q8_0

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": "khudgins/Ornith-1.0-35B-ThinkingCap-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use khudgins/Ornith-1.0-35B-ThinkingCap-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 khudgins/Ornith-1.0-35B-ThinkingCap-GGUF:Q8_0

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 khudgins/Ornith-1.0-35B-ThinkingCap-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use khudgins/Ornith-1.0-35B-ThinkingCap-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf khudgins/Ornith-1.0-35B-ThinkingCap-GGUF:Q8_0

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 "khudgins/Ornith-1.0-35B-ThinkingCap-GGUF:Q8_0" \
  --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 khudgins/Ornith-1.0-35B-ThinkingCap-GGUF with Docker Model Runner:
```
docker model run hf.co/khudgins/Ornith-1.0-35B-ThinkingCap-GGUF:Q8_0
```

Lemonade

How to use khudgins/Ornith-1.0-35B-ThinkingCap-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull khudgins/Ornith-1.0-35B-ThinkingCap-GGUF:Q8_0

Run and chat with the model

lemonade run user.Ornith-1.0-35B-ThinkingCap-GGUF-Q8_0

List all available models

lemonade list

Ornith-1.0-35B — Thinking-Cap — GGUF

This card describes the GGUF builds of khudgins/Ornith-1.0-35B-ThinkingCap — the ThinkingCap reasoning-efficiency fine-tune of deepreinforce-ai/Ornith-1.0-35B (a 256-expert Mixture-of-Experts coding model) — with the LoRA merged into the base and converted for llama.cpp / Ollama. Text-only (the base's vision tower is dropped in conversion).

Files

File	Quant	Size	Use
`Ornith-1.0-35B-ThinkingCap-Q8_0.gguf`	Q8_0	35 GB	Recommended — practical build; plan for ~40 GB RAM/VRAM
`Ornith-1.0-35B-ThinkingCap-f16.gguf`	F16	65 GB	Full-precision reference

Lower quants aren't provided here — on the 9B a Q4 of the base couldn't complete the eval, so Q8 is the floor I'd trust for this family.

Usage

Ollama (pulls the GGUF straight from this repo):

ollama run hf.co/khudgins/Ornith-1.0-35B-ThinkingCap-GGUF:Q8_0 "How many positive integers under 1000 are divisible by neither 5 nor 7?"

llama.cpp:

# pull + run
llama-cli   -hf khudgins/Ornith-1.0-35B-ThinkingCap-GGUF:Q8_0 -p "your prompt"
# or serve an OpenAI-compatible endpoint
llama-server -hf khudgins/Ornith-1.0-35B-ThinkingCap-GGUF:Q8_0

Fidelity

The tune's accuracy/token numbers (GSM8K 96% @ −24% tokens, HumanEval held, MBPP +2, avg 80.7% → 83.3%) were measured on the adapter via transformers — see the adapter card.

For the 9B, a matched f16-vs-Q8 Ollama sweep showed quantization was effectively free (−1.4 pts avg). For this 35B build I ran a coherence check on the Q8 rather than a full N=50 quant sweep — it reasons correctly (clean inclusion-exclusion derivations, correct answers) — so treat Q8 as the practical build and f16 as the precision reference, with the caveat that the quant delta here is un-swept rather than measured.

Notes

No speculative decoding — the Qwen3.5 MTP head is dropped for llama.cpp compatibility.
Reconverting from the merged HF model? On this MoE the MTP layer count lives in the nested config.text_config — set text_config.mtp_num_hidden_layers = 0 before converting, or the converter emits a phantom block (blk.40, block_count 41) and the GGUF fails to load with missing tensor 'blk.40.attn_norm.weight'. Full recipe in the tool repo (tools/).

License & provenance

MIT, same chain as the adapter: base deepreinforce-ai/Ornith-1.0-35B (MIT) → post-trained on Qwen3.5 (Apache-2.0). Method (an independent reproduction of BottleCap AI's ThinkingCap), training, and full results: see the adapter model card and the tool repo.

Downloads last month: 4

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

8-bit

16-bit

Model tree for khudgins/Ornith-1.0-35B-ThinkingCap-GGUF

Base model

deepreinforce-ai/Ornith-1.0-35B

Adapter

khudgins/Ornith-1.0-35B-ThinkingCap

Quantized

(1)

this model