Instructions to use deepreinforce-ai/Ornith-1.0-35B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepreinforce-ai/Ornith-1.0-35B-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="deepreinforce-ai/Ornith-1.0-35B-GGUF") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("deepreinforce-ai/Ornith-1.0-35B-GGUF", dtype="auto") - llama-cpp-python
How to use deepreinforce-ai/Ornith-1.0-35B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="deepreinforce-ai/Ornith-1.0-35B-GGUF", filename="ornith-1.0-35b-Q4_K_M.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 deepreinforce-ai/Ornith-1.0-35B-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 deepreinforce-ai/Ornith-1.0-35B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf deepreinforce-ai/Ornith-1.0-35B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf deepreinforce-ai/Ornith-1.0-35B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf deepreinforce-ai/Ornith-1.0-35B-GGUF: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 deepreinforce-ai/Ornith-1.0-35B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf deepreinforce-ai/Ornith-1.0-35B-GGUF: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 deepreinforce-ai/Ornith-1.0-35B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf deepreinforce-ai/Ornith-1.0-35B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/deepreinforce-ai/Ornith-1.0-35B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use deepreinforce-ai/Ornith-1.0-35B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "deepreinforce-ai/Ornith-1.0-35B-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": "deepreinforce-ai/Ornith-1.0-35B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/deepreinforce-ai/Ornith-1.0-35B-GGUF:Q4_K_M
- SGLang
How to use deepreinforce-ai/Ornith-1.0-35B-GGUF with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "deepreinforce-ai/Ornith-1.0-35B-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "deepreinforce-ai/Ornith-1.0-35B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "deepreinforce-ai/Ornith-1.0-35B-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "deepreinforce-ai/Ornith-1.0-35B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use deepreinforce-ai/Ornith-1.0-35B-GGUF with Ollama:
ollama run hf.co/deepreinforce-ai/Ornith-1.0-35B-GGUF:Q4_K_M
- Unsloth Studio
How to use deepreinforce-ai/Ornith-1.0-35B-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 deepreinforce-ai/Ornith-1.0-35B-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 deepreinforce-ai/Ornith-1.0-35B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for deepreinforce-ai/Ornith-1.0-35B-GGUF to start chatting
- Pi
How to use deepreinforce-ai/Ornith-1.0-35B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf deepreinforce-ai/Ornith-1.0-35B-GGUF: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": "deepreinforce-ai/Ornith-1.0-35B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use deepreinforce-ai/Ornith-1.0-35B-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 deepreinforce-ai/Ornith-1.0-35B-GGUF: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 deepreinforce-ai/Ornith-1.0-35B-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use deepreinforce-ai/Ornith-1.0-35B-GGUF with Docker Model Runner:
docker model run hf.co/deepreinforce-ai/Ornith-1.0-35B-GGUF:Q4_K_M
- Lemonade
How to use deepreinforce-ai/Ornith-1.0-35B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull deepreinforce-ai/Ornith-1.0-35B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Ornith-1.0-35B-GGUF-Q4_K_M
List all available models
lemonade list
31B-Dense model missing from HuggingFace
Hello, The model's card and blog post mentions a '31B-Dense' model, but it seems that is missing from HuggingFace? A 31B dense model (with MTP) in a single GGUF would be absolutely killer for agentic coding.
maybe there is some US government request regarding this 31B model as well? :)
modelscope.cn does not have 31B model , why?
"US government request" - are you joking they released full 397b model
of course I am joking 😀 (although nothing would surprise me anymore...)
This model resolved what qwen 27b q8 XL could not, and it's fast. It's not replacement by far.
But where is 31B dense model to try against qwen 27b q8 XL?
I hope 31B is delayed so they can also finetune the QAT version...
70b version would be the best killer, distill it, please
if these fine tunes distillations is not a scam altogether why nobody distills the Kimi 2.7 coder 1T model to make Qwen 3.6 27B better? The model is opensource nobody rents a server and does the job, seriously? And on kickstarter gets tons of money for random crap.
Distillation is imo misrepresented by some as a trivial process to arbitrarily transfer broad model capability. Today's frontier models (even the open-weights ones) are so delicate that you can most easily "break" them by further training, unless you just want to transfer/encourage some very narrow behavior/characteristic.
seems the base is qwen3.5, maybe qwen3.6-27B is better for the dense model base?
@weisunding yes, I'm assuming they did the fine tuning back before Qwen 3.6 was released perhaps? Regardless their 31B is based on Gemma 4 which typically hasn't been as good as Qwen 3.5/3.6 for coding but being a larger active model it would be very interesting to see how much they could improve from the base model.
from qwen3.5 to qwen3.6 or gemma4 ,about 1 month, so....
Tested for days now. NVFP4 with q8 cache MTP 3:
Qwen3.6:
- no loops
- no stuck
- better reasoning
- 100t/s
- 128k ctx
Ornith 1.0
- 300t/s
- some loops
- more errors
- sometimes stuck
- 300t/s
- 190K ctx
Well it is fast, but prone to errors. Have hopes for the dense 31b model.
Yes, I said I have high hopes for the dense model, since the moe fails
At this point I'm not sure the 31B dense model actually exists.