Instructions to use redstackio/qwen3-14b-redstack-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use redstackio/qwen3-14b-redstack-v1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="redstackio/qwen3-14b-redstack-v1",
	filename="qwen3-14b.Q5_K_M.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 redstackio/qwen3-14b-redstack-v1 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf redstackio/qwen3-14b-redstack-v1:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf redstackio/qwen3-14b-redstack-v1:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf redstackio/qwen3-14b-redstack-v1:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf redstackio/qwen3-14b-redstack-v1:Q5_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 redstackio/qwen3-14b-redstack-v1:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf redstackio/qwen3-14b-redstack-v1:Q5_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 redstackio/qwen3-14b-redstack-v1:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf redstackio/qwen3-14b-redstack-v1:Q5_K_M

Use Docker

docker model run hf.co/redstackio/qwen3-14b-redstack-v1:Q5_K_M

LM Studio
Jan

vLLM

How to use redstackio/qwen3-14b-redstack-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "redstackio/qwen3-14b-redstack-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "redstackio/qwen3-14b-redstack-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/redstackio/qwen3-14b-redstack-v1:Q5_K_M

Ollama
How to use redstackio/qwen3-14b-redstack-v1 with Ollama:
```
ollama run hf.co/redstackio/qwen3-14b-redstack-v1:Q5_K_M
```

Unsloth Studio new

How to use redstackio/qwen3-14b-redstack-v1 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 redstackio/qwen3-14b-redstack-v1 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 redstackio/qwen3-14b-redstack-v1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for redstackio/qwen3-14b-redstack-v1 to start chatting

Pi new

How to use redstackio/qwen3-14b-redstack-v1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf redstackio/qwen3-14b-redstack-v1:Q5_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": "redstackio/qwen3-14b-redstack-v1:Q5_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use redstackio/qwen3-14b-redstack-v1 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf redstackio/qwen3-14b-redstack-v1:Q5_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 redstackio/qwen3-14b-redstack-v1:Q5_K_M

Run Hermes

hermes

Docker Model Runner
How to use redstackio/qwen3-14b-redstack-v1 with Docker Model Runner:
```
docker model run hf.co/redstackio/qwen3-14b-redstack-v1:Q5_K_M
```

Lemonade

How to use redstackio/qwen3-14b-redstack-v1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull redstackio/qwen3-14b-redstack-v1:Q5_K_M

Run and chat with the model

lemonade run user.qwen3-14b-redstack-v1-Q5_K_M

List all available models

lemonade list

LSB127 commited on 29 days ago

Commit

97d0cfa

verified ·

1 Parent(s): fe41323

Rename README and updated

Browse files

Files changed (1) hide show

README_14b.md → README.md +16 -3

README_14b.md → README.md RENAMED Viewed

@@ -1,6 +1,11 @@
 ---
 license: other
 base_model: Qwen/Qwen3-14B
 tags:
   - gguf
   - qwen3
@@ -8,7 +13,6 @@ tags:
   - security
   - lora
   - sft
-library_name: gguf
 ---
 # Zero Stack - Qwen3-14B (GGUF, Q5_K_M)
@@ -36,6 +40,15 @@ ollama run zerostack-14b
 - Max sequence length: 2560
 - Dataset: SFT_GENERALIST (1,226 rows, ChatML)
-## License / Use
-For authorized security testing, research, and educational use only. Attribution to RedStack required. Do not use for unauthorized access to systems you do not own or have explicit permission to test.

 ---
 license: other
+pipeline_tag: text-generation
+library_name: gguf
+language:
+  - en
 base_model: Qwen/Qwen3-14B
+base_model_relation: quantized
 tags:
   - gguf
   - qwen3
   - security
   - lora
   - sft
 ---
 # Zero Stack - Qwen3-14B (GGUF, Q5_K_M)
 - Max sequence length: 2560
 - Dataset: SFT_GENERALIST (1,226 rows, ChatML)
+## Intended Use
+Authorized security testing, CTF practice, red-team research, and security education. Targeted at practitioners who already know what they're doing and want structured methodology and command recall.
+## Limitations & Risks
+- May hallucinate specific CVE IDs, tool flags, or payload syntax - verify against primary sources before running.
+- No safety guardrails against misuse. Do not use against systems you don't own or have explicit written authorization to test.
+- Thinking mode is on by default - responses may be slower and include reasoning traces. Disable in Modelfile if you want faster, terser output.
+- Trained on English data only; non-English performance is not evaluated.
+- 16 GB VRAM note: GGUF export uses CPU offloading to avoid LoRA merge corruption. If you retrain/re-export, verify `maximum_memory_usage=0.5` in `export_gguf.py`.
+## License / Use
+For authorized security testing, research, and educational use only. Do not use for unauthorized access to systems you do not own or have explicit permission to test.