Instructions to use prithivMLmods/Qwen3.5-27B-MTP-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Qwen3.5-27B-MTP-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="prithivMLmods/Qwen3.5-27B-MTP-GGUF") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("prithivMLmods/Qwen3.5-27B-MTP-GGUF", dtype="auto") - llama-cpp-python
How to use prithivMLmods/Qwen3.5-27B-MTP-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="prithivMLmods/Qwen3.5-27B-MTP-GGUF", filename="Qwen3.5-27B.BF16.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 prithivMLmods/Qwen3.5-27B-MTP-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf prithivMLmods/Qwen3.5-27B-MTP-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf prithivMLmods/Qwen3.5-27B-MTP-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf prithivMLmods/Qwen3.5-27B-MTP-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf prithivMLmods/Qwen3.5-27B-MTP-GGUF:Q4_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 prithivMLmods/Qwen3.5-27B-MTP-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf prithivMLmods/Qwen3.5-27B-MTP-GGUF:Q4_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 prithivMLmods/Qwen3.5-27B-MTP-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf prithivMLmods/Qwen3.5-27B-MTP-GGUF:Q4_K_M
Use Docker
docker model run hf.co/prithivMLmods/Qwen3.5-27B-MTP-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use prithivMLmods/Qwen3.5-27B-MTP-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "prithivMLmods/Qwen3.5-27B-MTP-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": "prithivMLmods/Qwen3.5-27B-MTP-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/prithivMLmods/Qwen3.5-27B-MTP-GGUF:Q4_K_M
- SGLang
How to use prithivMLmods/Qwen3.5-27B-MTP-GGUF with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "prithivMLmods/Qwen3.5-27B-MTP-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "prithivMLmods/Qwen3.5-27B-MTP-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 images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "prithivMLmods/Qwen3.5-27B-MTP-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "prithivMLmods/Qwen3.5-27B-MTP-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" } } ] } ] }' - Ollama
How to use prithivMLmods/Qwen3.5-27B-MTP-GGUF with Ollama:
ollama run hf.co/prithivMLmods/Qwen3.5-27B-MTP-GGUF:Q4_K_M
- Unsloth Studio
How to use prithivMLmods/Qwen3.5-27B-MTP-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 prithivMLmods/Qwen3.5-27B-MTP-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 prithivMLmods/Qwen3.5-27B-MTP-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for prithivMLmods/Qwen3.5-27B-MTP-GGUF to start chatting
- Pi
How to use prithivMLmods/Qwen3.5-27B-MTP-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf prithivMLmods/Qwen3.5-27B-MTP-GGUF:Q4_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": "prithivMLmods/Qwen3.5-27B-MTP-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use prithivMLmods/Qwen3.5-27B-MTP-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf prithivMLmods/Qwen3.5-27B-MTP-GGUF:Q4_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 prithivMLmods/Qwen3.5-27B-MTP-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use prithivMLmods/Qwen3.5-27B-MTP-GGUF with Docker Model Runner:
docker model run hf.co/prithivMLmods/Qwen3.5-27B-MTP-GGUF:Q4_K_M
- Lemonade
How to use prithivMLmods/Qwen3.5-27B-MTP-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull prithivMLmods/Qwen3.5-27B-MTP-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Qwen3.5-27B-MTP-GGUF-Q4_K_M
List all available models
lemonade list
Qwen3.5-27B-MTP-GGUF
Qwen3.5-27B from Alibaba's Qwen team is a 27B-parameter dense (non-MoE) multimodal transformer model—the only full-weight, non-MoE model in the Qwen3.5 Medium Series—featuring 64 layers, 5120 hidden dimension, 248K vocabulary for 201+ languages, native multimodal input (text + images + video via early fusion), and a massive 256K native context window extensible to 1M+ tokens via YaRN. Released February 24, 2026 under Apache 2.0, it achieves 72.4% on SWE-bench Verified (matching GPT-5 mini), 87.8% GPQA Diamond, 66.1% BFCL-V4 for native tool calling, and excels at agentic coding, frontend development, repository-level code comprehension, and complex reasoning while running on 22GB VRAM (Mac M-series, single RTX 4090) with simple deployment free of MoE routing overhead. Its strengths include native multimodal chat, long-context document processing, structured JSON outputs, fine-tuning for specialized domains, and Ollama/vLLM/llama.cpp support, making it ideal for production multimodal assistants combining image understanding with grounded text reasoning at efficient scale.
Multi-Token Prediction (MTP) GGUF is a specialized GGUF model file format extension that integrates speculative decoding directly into the model weights to significantly accelerate local inference. Unlike traditional speculative decoding which requires a separate, smaller "draft" model, MTP GGUF files include additional output heads within the main model architecture that predict multiple future tokens in a single forward pass.
Model Files
| File Name | Quant Type | File Size | File Link |
|---|---|---|---|
| Qwen3.5-27B.BF16.gguf | BF16 | 54.7 GB | Download |
| Qwen3.5-27B.F16.gguf | F16 | 54.7 GB | Download |
| Qwen3.5-27B.Q2_K.gguf | Q2_K | 10.9 GB | Download |
| Qwen3.5-27B.Q3_K_L.gguf | Q3_K_L | 14.6 GB | Download |
| Qwen3.5-27B.Q3_K_M.gguf | Q3_K_M | 13.5 GB | Download |
| Qwen3.5-27B.Q3_K_S.gguf | Q3_K_S | 12.3 GB | Download |
| Qwen3.5-27B.Q4_0.gguf | Q4_0 | 15.7 GB | Download |
| Qwen3.5-27B.Q4_K_M.gguf | Q4_K_M | 16.8 GB | Download |
| Qwen3.5-27B.Q4_K_S.gguf | Q4_K_S | 15.8 GB | Download |
| Qwen3.5-27B.Q5_0.gguf | Q5_0 | 19 GB | Download |
| Qwen3.5-27B.Q5_K_M.gguf | Q5_K_M | 19.5 GB | Download |
| Qwen3.5-27B.Q5_K_S.gguf | Q5_K_S | 19 GB | Download |
| Qwen3.5-27B.Q6_K.gguf | Q6_K | 22.4 GB | Download |
| Qwen3.5-27B.Q8_0.gguf | Q8_0 | 29 GB | Download |
| Qwen3.5-27B.mmproj-bf16.gguf | mmproj-bf16 | 931 MB | Download |
| Qwen3.5-27B.mmproj-f16.gguf | mmproj-f16 | 931 MB | Download |
| Qwen3.5-27B.mmproj-q8_0.gguf | mmproj-q8_0 | 629 MB | Download |
Quants Usage
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):
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Model tree for prithivMLmods/Qwen3.5-27B-MTP-GGUF
Base model
Qwen/Qwen3.5-27B