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
Any chance to get 122b version?
#31 opened about 3 hours ago
by
auf1r2
Delete README.md
#30 opened about 3 hours ago
by
Pq234
Delete README.md
#29 opened about 3 hours ago
by
Pq234
Delete README.md
#28 opened about 3 hours ago
by
Pq234
μ..λͺ¨λΈ λ무 μ’μ΅λλ€.
1
#27 opened 1 day ago
by
qgame
Model gets stuck in recursive loop
π 8
3
#26 opened 1 day ago
by
mthenhaus
Fix for prompt processing error in LM Studio
#25 opened 1 day ago
by
TESTPOINTrxz
I don't really have a question..
#24 opened 1 day ago
by
CurbStomper
Aider Polyglot Benchmark Results (C++ & Python) β Q4 through Q8
π 5
2
#23 opened 2 days ago
by
YukiTomita-CC
Reasoning degradation and tool calling failures at 70-80K context tokens
20
#22 opened 2 days ago
by
Sdoh
Having `encoding` problem with bash commands and paths all the way...
#20 opened 2 days ago
by
gbuzhf
Can not use 35B with claude code and codex.
1
#19 opened 2 days ago
by
beginor
Same tool call issue.
2
#18 opened 3 days ago
by
mehedimi
Excellent
β€οΈ 2
2
#17 opened 3 days ago
by
Hangover3832
Ornith problems so far
2
#16 opened 3 days ago
by
ajeetcoolkarni
Getting better speeds and somehow results then Qwen3.6-35B-A3B! Q4
π 1
1
#15 opened 4 days ago
by
SaturnsVoid
This model is amazing and def better than base model.
π₯π 4
#14 opened 4 days ago
by
anubhav200
After loading this model, the agent falls into an infinite loop during tool calls, or the response gets cut off within the first three turns.
7
#13 opened 4 days ago
by
AntonioWen
A great model, but...
2
#10 opened 5 days ago
by
hklamaa
Disappointed. Terrible for Agentic Coding Harness
5
#9 opened 5 days ago
by
darkmatter2222
Good Job
ππ₯ 6
#8 opened 5 days ago
by
islameissa
Thank you! This is an excellent model!
π 2
#7 opened 5 days ago
by
chinabop
Issue with the chat template in opencode
1
#6 opened 5 days ago
by
Milor123
This is based on Qwen3.5 and Gemma 4, are you planning something like Qwen3.6+Gemma 4?
π 11
1
#5 opened 5 days ago
by
Kalemnor
MTP weights
ββ€οΈ 19
2
#4 opened 5 days ago
by
scottgl
mmproj-*.gguf
2
#3 opened 5 days ago
by
sjwwork
31B-Dense model missing from HuggingFace
ππ 25
14
#2 opened 5 days ago
by
smcleod
So much expectations from a small model
3
#1 opened 6 days ago
by
hamadfx