Instructions to use D0shi9/Barq-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use D0shi9/Barq-27B 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 D0shi9/Barq-27B:Q2_0 # Run inference directly in the terminal: llama cli -hf D0shi9/Barq-27B:Q2_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf D0shi9/Barq-27B:Q2_0 # Run inference directly in the terminal: llama cli -hf D0shi9/Barq-27B:Q2_0
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 D0shi9/Barq-27B:Q2_0 # Run inference directly in the terminal: ./llama-cli -hf D0shi9/Barq-27B:Q2_0
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 D0shi9/Barq-27B:Q2_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf D0shi9/Barq-27B:Q2_0
Use Docker
docker model run hf.co/D0shi9/Barq-27B:Q2_0
- LM Studio
- Jan
- vLLM
How to use D0shi9/Barq-27B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "D0shi9/Barq-27B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "D0shi9/Barq-27B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/D0shi9/Barq-27B:Q2_0
- Ollama
How to use D0shi9/Barq-27B with Ollama:
ollama run hf.co/D0shi9/Barq-27B:Q2_0
- Unsloth Desktop
- Pi
How to use D0shi9/Barq-27B with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf D0shi9/Barq-27B:Q2_0
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "D0shi9/Barq-27B:Q2_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use D0shi9/Barq-27B with Docker Model Runner:
docker model run hf.co/D0shi9/Barq-27B:Q2_0
- Lemonade
How to use D0shi9/Barq-27B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull D0shi9/Barq-27B:Q2_0
Run and chat with the model
lemonade run user.Barq-27B-Q2_0
List all available models
lemonade list
- Hermes Agent
How to use D0shi9/Barq-27B with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf D0shi9/Barq-27B:Q2_0
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 D0shi9/Barq-27B:Q2_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use D0shi9/Barq-27B with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf D0shi9/Barq-27B:Q2_0
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 "D0shi9/Barq-27B:Q2_0" \ --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"
Barq 27B | برق
Barq 27B is a local GGUF distribution with a Barq operating policy and a chat-template adapter embedded in the language model. It is derived from Prism ML's Ternary Bonsai 2 27B GGUF, whose upstream model card identifies Qwen3.8-27B as its backbone.
The numerical tensor weights are unchanged. Barq does not claim additional training, a new weight quantization method, or an improvement over the upstream model's benchmark results. Its changes are in the operating policy, chat template, and descriptive GGUF metadata.
Files
| File | Purpose | Approximate download size |
|---|---|---|
Barq-27B-PQ2_0.gguf |
Language model with the embedded Barq policy and template | 7.21 GB |
Barq-27B-mmproj-Q8_0.gguf |
Optional vision projector for image input in a compatible runtime | 0.63 GB |
USAGE.md |
Runtime requirements and local serving examples | Text |
LICENSE |
Apache License 2.0 | Text |
NOTICE |
Attribution and modification notice | Text |
SHA256SUMS.txt |
Integrity checksums for the other files | Text |
Download sizes use decimal GB and are not RAM or VRAM requirements. Text-only use does not require the projector. Runtime memory also depends on the context, cache configuration, backend, and other allocations.
Runtime compatibility
Use Prism ML's modified llama.cpp runtime with support for the PQ2_0 ternary format and its activation transforms. The upstream model card states that stock llama.cpp cannot run these files correctly. Generic GGUF support alone is insufficient.
See USAGE.md and the upstream Bonsai demo for setup. Runtime binaries are obtained separately.
Intended use and application responsibilities
The embedded policy is intended for conversation, explanations, coding assistance, and planning. Arabic and English are target interaction languages; this statement is not a language-quality benchmark.
Tool calls require a host application that supplies tool definitions, validates requests, executes permitted actions, and returns results. A generated tool call does not itself run a command or access a device or account.
Persistent memory, MCP connections, agent orchestration, and the six named profiles with enforced token budgets are application features. A host must implement them; they are not supplied as services by these GGUF files. References to host features in the embedded policy apply only when that host actually provides them.
The host should preserve the embedded chat template. Replacing it or bypassing it changes how the Barq policy is applied. Prompts are guidance, not an authorization or security boundary.
Evaluation and limitations
No Barq-specific benchmark scores are published in this repository. Upstream scores are not presented as measurements of this distribution. No comparative speed, accuracy, reliability, or hardware-fit guarantee is made.
Model outputs can be incorrect, including generated code and image interpretations. Review outputs and validate proposed actions before relying on them. Image input requires the supplied projector and a compatible multimodal host. Compatibility with every GGUF application is not implied.
License and attribution
Distributed under the Apache License 2.0. See NOTICE for attribution and the scope of modifications. Original model authorship is retained; Barq does not imply endorsement or affiliation with upstream vendors.
العربية
برق 27B نسخة GGUF محلية مشتقة من النموذج الأصلي لـ Prism ML، تتضمن سياسة تشغيل وقالب محادثة خاصين ببرق. الأوزان العددية لم تتغير، ولا يُدّعى تدريب إضافي أو تفوق مقاس على النموذج الأصلي.
يلزم محرك Prism ML المتوافق مع PQ2_0. ملف الصور اختياري للمحادثة النصية. تنفيذ الأدوات والذاكرة الدائمة وMCP والوكلاء يحتاج تطبيقاً مضيفاً يوفر هذه الوظائف فعلياً. تفاصيل التشغيل في USAGE.md.
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