Instructions to use lancejames221b/razorstrike-v1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lancejames221b/razorstrike-v1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="lancejames221b/razorstrike-v1-GGUF",
	filename="mmproj.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use lancejames221b/razorstrike-v1-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 lancejames221b/razorstrike-v1-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf lancejames221b/razorstrike-v1-GGUF:IQ4_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf lancejames221b/razorstrike-v1-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf lancejames221b/razorstrike-v1-GGUF:IQ4_XS

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 lancejames221b/razorstrike-v1-GGUF:IQ4_XS
# Run inference directly in the terminal:
./llama-cli -hf lancejames221b/razorstrike-v1-GGUF:IQ4_XS

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 lancejames221b/razorstrike-v1-GGUF:IQ4_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lancejames221b/razorstrike-v1-GGUF:IQ4_XS

Use Docker

docker model run hf.co/lancejames221b/razorstrike-v1-GGUF:IQ4_XS

LM Studio
Jan

vLLM

How to use lancejames221b/razorstrike-v1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lancejames221b/razorstrike-v1-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": "lancejames221b/razorstrike-v1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/lancejames221b/razorstrike-v1-GGUF:IQ4_XS

Ollama
How to use lancejames221b/razorstrike-v1-GGUF with Ollama:
```
ollama run hf.co/lancejames221b/razorstrike-v1-GGUF:IQ4_XS
```

Unsloth Studio

How to use lancejames221b/razorstrike-v1-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 lancejames221b/razorstrike-v1-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 lancejames221b/razorstrike-v1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for lancejames221b/razorstrike-v1-GGUF to start chatting

How to use lancejames221b/razorstrike-v1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lancejames221b/razorstrike-v1-GGUF:IQ4_XS

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": "lancejames221b/razorstrike-v1-GGUF:IQ4_XS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use lancejames221b/razorstrike-v1-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 lancejames221b/razorstrike-v1-GGUF:IQ4_XS

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 lancejames221b/razorstrike-v1-GGUF:IQ4_XS

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use lancejames221b/razorstrike-v1-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lancejames221b/razorstrike-v1-GGUF:IQ4_XS

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 "lancejames221b/razorstrike-v1-GGUF:IQ4_XS" \
  --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 lancejames221b/razorstrike-v1-GGUF with Docker Model Runner:
```
docker model run hf.co/lancejames221b/razorstrike-v1-GGUF:IQ4_XS
```

Lemonade

How to use lancejames221b/razorstrike-v1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lancejames221b/razorstrike-v1-GGUF:IQ4_XS

Run and chat with the model

lemonade run user.razorstrike-v1-GGUF-IQ4_XS

List all available models

lemonade list

lancejames221b commited on 9 days ago

Commit

174afb7

verified ·

1 Parent(s): 7395c31

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+---
+license: apache-2.0
+base_model:
+  - Qwen/Qwen3.5-35B-A3B-Base
+  - deepreinforce-ai/Ornith-1.0-35B
+tags:
+  - qwen3_5_moe
+  - gguf
+  - moe
+  - reasoning
+  - agentic
+  - security
+  - llama.cpp
+  - ollama
+language:
+  - en
+---
+# RazorStrike-v1 (GGUF, Q4_K_M)
+Text-only GGUF quantization of `razorstrike-v1` (the MLX 4-bit multimodal original). Vision tower is not
+included in GGUF (text-only inference).
+A multi-donor task-arithmetic merge on the **Qwen3.5-35B-A3B** MoE base (hybrid linear-attention/SSM,
+256 experts).
+## Composition
+- **Base:** `ornith-agents1` = Ornith-1.0-35B (reasoning) + Agents-A1 (agentic-coding; router taken verbatim
+  from Agents-A1)
+- **+ Opus-Reasoning** distilled donor @ weight 0.30 (task-arithmetic)
+- **+ AgentWorld** terminal/OS grounding @ weight 0.10 (task-arithmetic)
+## Requires
+This model uses the `qwen35moe` GGUF architecture (`Qwen3_5MoeForConditionalGeneration`). Requires a
+**recent llama.cpp / Ollama build with Qwen3.5 MoE support**. `Q4_K_M`, 20.2G, 733 tensors.
+## Recommended settings
+`temp 0.6, top_p 0.95, top_k 20, repeat_penalty 1.1`. Give a generous `max_tokens`/`num_predict` (≥2000) —
+reasoning donors mean the model thinks before answering; a small budget can yield empty output. Use a
+"think fast, not long" system prompt to keep responses tight.
+## Ollama
+GGUF is Ollama-compatible (`qwen35moe` arch); registry namespace pending publication.
+```
+ollama pull hf.co/lancejames221b/razorstrike-v1-GGUF
+```
+## License
+`apache-2.0` (umbrella). Ornith component is MIT; Opus donor distilled from Claude-4.7-Opus (confirm upstream
+usage terms for your use case).

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