Instructions to use DJLougen/Ornstein3.6-35B-A3B-RYS-SABER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DJLougen/Ornstein3.6-35B-A3B-RYS-SABER with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="DJLougen/Ornstein3.6-35B-A3B-RYS-SABER",
	filename="Ornstein3.6-35B-A3B-RYS-SABER-BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use DJLougen/Ornstein3.6-35B-A3B-RYS-SABER with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf DJLougen/Ornstein3.6-35B-A3B-RYS-SABER:BF16
# Run inference directly in the terminal:
llama-cli -hf DJLougen/Ornstein3.6-35B-A3B-RYS-SABER:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf DJLougen/Ornstein3.6-35B-A3B-RYS-SABER:BF16
# Run inference directly in the terminal:
llama-cli -hf DJLougen/Ornstein3.6-35B-A3B-RYS-SABER:BF16

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 DJLougen/Ornstein3.6-35B-A3B-RYS-SABER:BF16
# Run inference directly in the terminal:
./llama-cli -hf DJLougen/Ornstein3.6-35B-A3B-RYS-SABER:BF16

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 DJLougen/Ornstein3.6-35B-A3B-RYS-SABER:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DJLougen/Ornstein3.6-35B-A3B-RYS-SABER:BF16

Use Docker

docker model run hf.co/DJLougen/Ornstein3.6-35B-A3B-RYS-SABER:BF16

LM Studio
Jan

vLLM

How to use DJLougen/Ornstein3.6-35B-A3B-RYS-SABER with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DJLougen/Ornstein3.6-35B-A3B-RYS-SABER"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DJLougen/Ornstein3.6-35B-A3B-RYS-SABER",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DJLougen/Ornstein3.6-35B-A3B-RYS-SABER:BF16

Ollama
How to use DJLougen/Ornstein3.6-35B-A3B-RYS-SABER with Ollama:
```
ollama run hf.co/DJLougen/Ornstein3.6-35B-A3B-RYS-SABER:BF16
```

Unsloth Studio new

How to use DJLougen/Ornstein3.6-35B-A3B-RYS-SABER 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 DJLougen/Ornstein3.6-35B-A3B-RYS-SABER 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 DJLougen/Ornstein3.6-35B-A3B-RYS-SABER to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DJLougen/Ornstein3.6-35B-A3B-RYS-SABER to start chatting

Pi new

How to use DJLougen/Ornstein3.6-35B-A3B-RYS-SABER with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf DJLougen/Ornstein3.6-35B-A3B-RYS-SABER:BF16

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": "Ornstein3.6-35B-A3B-RYS-SABER"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use DJLougen/Ornstein3.6-35B-A3B-RYS-SABER with Docker Model Runner:
```
docker model run hf.co/DJLougen/Ornstein3.6-35B-A3B-RYS-SABER:BF16
```

Lemonade

How to use DJLougen/Ornstein3.6-35B-A3B-RYS-SABER with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DJLougen/Ornstein3.6-35B-A3B-RYS-SABER:BF16

Run and chat with the model

lemonade run user.Ornstein3.6-35B-A3B-RYS-SABER-BF16

List all available models

lemonade list

DJLougen commited on 21 days ago

Commit

eb3dc88

verified ·

1 Parent(s): 74d0d45

README: note that thinking model needs bundled chat_template.jinja + --jinja --reasoning-format deepseek to hide <think> tags

Browse files

Files changed (1) hide show

README.md +17 -0

README.md CHANGED Viewed

@@ -35,6 +35,23 @@ This release ships as **BF16 GGUF re-converted with per-layer `layer_types` bake
 The patch is a single commit on top of `ggml-org/llama.cpp@d00685831`. It is backward-compatible — stock (non-RYS) Qwen3.5 GGUFs still load normally.
 ## Support This Work
 I'm a PhD student in visual neuroscience at the University of Toronto who also happens to spend way too much time fine-tuning, merging, and quantizing open-weight models on rented H100s and a local DGX Spark. All training compute is self-funded — balancing GPU costs against a student budget. If my uploads have been useful to you, consider buying a PhD student a coffee. It goes a long way toward keeping these experiments running.

 The patch is a single commit on top of `ggml-org/llama.cpp@d00685831`. It is backward-compatible — stock (non-RYS) Qwen3.5 GGUFs still load normally.
+## Note: thinking model — use the bundled `chat_template.jinja`
+This is a Qwen3-Thinking derivative and emits its reasoning inside `<think>...</think>` tags. If you see raw `<think>thinking text</think>` blocks appearing inline in every response from `llama-server` (or any OpenAI-compatible client), you need to apply the Qwen3 thinking chat template that ships in this repo.
+```bash
+llama-server \
+    -m Ornstein3.6-35B-A3B-RYS-SABER-BF16.gguf \
+    --jinja \
+    --chat-template-file chat_template.jinja \
+    --reasoning-format deepseek \
+    -ngl 99 -c 8192
+```
+- `--jinja` enables jinja chat-template parsing.
+- `--chat-template-file chat_template.jinja` overrides whatever template is embedded in the GGUF with the correct Qwen3-Thinking one from this repo.
+- `--reasoning-format deepseek` makes llama-server split `<think>...</think>` out into a separate `reasoning_content` field on the response instead of leaving it inline in `content`. Without this flag the tags will still appear in the response body even with the right template.
 ## Support This Work
 I'm a PhD student in visual neuroscience at the University of Toronto who also happens to spend way too much time fine-tuning, merging, and quantizing open-weight models on rented H100s and a local DGX Spark. All training compute is self-funded — balancing GPU costs against a student budget. If my uploads have been useful to you, consider buying a PhD student a coffee. It goes a long way toward keeping these experiments running.