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

razorstrike-v1-GGUF / README.md

lancejames221b

Fix cross-repo model card inconsistencies: correct lineage cross-references across razorstrike-v1 / -bf16 / -GGUF

2b174f0 verified 5 days ago

preview code

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metadata

license: apache-2.0
base_model:
  - Qwen/Qwen3.6-35B-A3B
  - nightmedia/Qwen3.6-35B-A3B-Holo3-Qwopus-AgentWorld-qx64-hi-mlx
  - Hcompany/Holo3-35B-A3B
  - samuelcardillo/Qwopus-MoE-35B-A3B
  - Qwen/Qwen-AgentWorld-35B-A3B
  - llmfan46/Qwen3.6-35B-A3B-uncensored-heretic-Native-MTP-Preserved
tags:
  - gguf
  - llama.cpp
  - qwen3_5_moe
  - moe
  - merge
  - vision-language
  - security
  - image-text-to-text

RazorStrike-v1 GGUF

GGUF build of lancejames221b/razorstrike-v1. Same lineage as the current MLX repo, not the older DARE-TIES build documented in lancejames221b/razorstrike-v1-bf16 (that lineage was superseded 2026-07-21 and has no GGUF/MLX quant under this org).

Base: a 4-bit-equivalent quantization of nightmedia/Qwen3.6-35B-A3B-Holo3-Qwopus-AgentWorld-qx64-hi-mlx (itself a merge of Hcompany/Holo3-35B-A3B, samuelcardillo/Qwopus-MoE-35B-A3B, and llmfan46/Qwen3.6-35B-A3B-uncensored-heretic-Native-MTP-Preserved with Qwen/Qwen-AgentWorld-35B-A3B) with the Qwen3.6-35B-A3B vision tower spliced back in — see lancejames221b/razorstrike-v1's README for the full provenance and known-issues notes (including the inherited repetition/looping caveat).

Files

razorstrike-v1-IQ4_XS.gguf — 4.37 BPW IQ4_XS text model, converted with llama.cpp from a raw-HF-compatible reconstruction of the source above.
mmproj.gguf — matching multimodal projector for image input.
RAZORSTRIKE_V1_GGUF_MANIFEST.json — build and verification notes.

The bf16 GGUF intermediate was generated and smoke-tested locally but is not uploaded because it is ~65 GiB.

Fix in this build

Previous GGUF attempts generated corrupted text because the source checkpoint was MLX-derived. MLX stores non-linear-attention text RMSNorm weights with the +1.0 offset already baked in, while the llama.cpp Qwen converter expects raw HF-style weights and applies its own +1.0 transform for those tensors. This build reconstructs the HF-naming checkpoint with:

model.language_model.*norm.weight shifted by -1.0, except linear_attn.norm.weight.
linear_attn.norm.weight kept unchanged.
vision tower norms kept unchanged.
MLX switch_mlp.{gate,up}_proj.weight merged into raw HF experts.gate_up_proj.
MLX switch_mlp.down_proj.weight renamed to raw HF experts.down_proj.
llama.cpp conversion run with --no-mtp to avoid false extra-layer metadata.

Local verification

Verified on the fixed IQ4_XS GGUF through llama-server:

llama-server \
  -m razorstrike-v1-IQ4_XS.gguf \
  --mmproj mmproj.gguf \
  -c 4096 \
  -fit off

Text smoke test:

System: <|think_off|>
User: What is 17 times 24? Answer directly.
Assistant: 408

Image smoke test with a generated PNG containing a green square, yellow circle, and TEST-42:

Assistant:
- Green square
- Yellow circle
- Text: "TEST-42"

License

Apache-2.0, matching the Qwen3.6 lineage and the current RazorStrike-v1 model card.