Image-Text-to-Text
GGUF
llama.cpp
qwen3_5_moe
Mixture of Experts
Merge
vision-language
security
conversational
Instructions to use lancejames221b/razorstrike-v1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
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
- Pi
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
File size: 2,949 Bytes
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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`:
```bash
llama-server \
-m razorstrike-v1-IQ4_XS.gguf \
--mmproj mmproj.gguf \
-c 4096 \
-fit off
```
Text smoke test:
```text
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`:
```text
Assistant:
- Green square
- Yellow circle
- Text: "TEST-42"
```
## License
Apache-2.0, matching the Qwen3.6 lineage and the current RazorStrike-v1 model card.
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