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

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

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

Model gets stuck in recursive loop

#26

by mthenhaus - opened 3 days ago

Discussion

mthenhaus

3 days ago

When the context windows exceeds about 22,000 tokens the model will start recursive generation, outputting the same tokens over and over again in a loop until you hard interrupt. Running on Llama.cpp with the BF16 quant. This is basically useless.

twizzyrich

3 days ago

•

edited 3 days ago

I have same behaviour on Q4 quantization with llama cpp 😞

Using custom jinja template from here doesn't help.

viiri2

2 days ago

•

edited 2 days ago

I'm having success on 27B MTP variant Qwopus using the original chat template: https://huggingface.co/Qwen/Qwen3.6-27B/blob/main/chat_template.jinja. Perhaps 35B one is similar? No hallucinations caused by quantization when k-cache is kept at bf16 and Q5_K_M following the recommended sampling parameters and using preserve_thinking option.

EDIT: nevermind, my template was for 3.6, while this is likely 3.5 based model.

Sdoh

1 day ago

When the context windows exceeds about 22,000 tokens the model will start recursive generation, outputting the same tokens over and over again in a loop until you hard interrupt. Running on Llama.cpp with the BF16 quant. This is basically useless.

Привет. У меня linux, rx6800. Модель работает хорошо только с драйвером ROCm .
Если у тебя тоже AMD - надо разбираться с драйверами и методом запуска.

Вот как я запускаю:

/run/media/... /llama-ROCm/llama-server
-m "/run/media/... /Ornith-1.0-35B/ornith-1.0-35b-Q4_K_M.gguf"
--host 0.0.0.0 --port 1338 --api-key sk-1234
--temp 1.0 --top-p 0.95 --top-k 20 --min-p 0.0 --presence-penalty 0.0 --frequency-penalty 0.0 --repeat-penalty 1.0
--threads 8 --threads-batch 16 --no-mmap -fa on
--parallel 1 -cb --kv_unified
--ubatch-size 1024
--batch-size 1024
--jinja
--chat-template-file "/run/media/... /Qwen3.6/chat_template_V20.jinja"
--chat-template-kwargs '{"enable_thinking":true, "preserve_thinking":true, "auto_disable_thinking_with_tools":false}'
--fit on --fit-ctx 131072 --fit-target 2560
--cache-type-k q8_0 --cache-type-v q8_0 \

somespecialone

1 day ago

Got same error in Q4_K_M (using https://github.com/TheTom/llama-cpp-turboquant with). Find out k-cache and v-cache quantization cause this behavior, surely not template alone.

parseco

about 9 hours ago

When the context windows exceeds about 22,000 tokens the model will start recursive generation, outputting the same tokens over and over again in a loop until you hard interrupt. Running on Llama.cpp with the BF16 quant. This is basically useless.

Hola, agrega una restricción en el "System prompt" de LM Studio, esto puede mejorar mucho la respuesta y evitar el loop: por ejemplo, agrega lo siguiente:
expert role, uses CoT

En LM Studio funcionó muy bien para mí, cuéntame si te sirvió.

parseco

about 9 hours ago

I have same behaviour on Q4 quantization with llama cpp 😞

Using custom jinja template from here doesn't help.

Hola, prueba lo siguiente, agrega una restricción en el "System prompt" de LM Studio, esto puede mejorar mucho la respuesta y evitar el loop: por ejemplo, agrega lo siguiente:
expert role, uses CoT

En LM Studio funcionó muy bien para mí, cuéntame si te sirvió.

Hice muchas pruebas para llegar a esa mejora.

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