Instructions to use AesSedai/Qwen3.5-122B-A10B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use AesSedai/Qwen3.5-122B-A10B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="AesSedai/Qwen3.5-122B-A10B-GGUF", filename="IQ2_XXS/Qwen3.5-122B-A10B-IQ2_XXS-00001-of-00002.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use AesSedai/Qwen3.5-122B-A10B-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf AesSedai/Qwen3.5-122B-A10B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf AesSedai/Qwen3.5-122B-A10B-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 AesSedai/Qwen3.5-122B-A10B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf AesSedai/Qwen3.5-122B-A10B-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 AesSedai/Qwen3.5-122B-A10B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf AesSedai/Qwen3.5-122B-A10B-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 AesSedai/Qwen3.5-122B-A10B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf AesSedai/Qwen3.5-122B-A10B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/AesSedai/Qwen3.5-122B-A10B-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use AesSedai/Qwen3.5-122B-A10B-GGUF with Ollama:
ollama run hf.co/AesSedai/Qwen3.5-122B-A10B-GGUF:Q4_K_M
- Unsloth Studio
How to use AesSedai/Qwen3.5-122B-A10B-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 AesSedai/Qwen3.5-122B-A10B-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 AesSedai/Qwen3.5-122B-A10B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for AesSedai/Qwen3.5-122B-A10B-GGUF to start chatting
- Pi
How to use AesSedai/Qwen3.5-122B-A10B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf AesSedai/Qwen3.5-122B-A10B-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": "AesSedai/Qwen3.5-122B-A10B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use AesSedai/Qwen3.5-122B-A10B-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 AesSedai/Qwen3.5-122B-A10B-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 AesSedai/Qwen3.5-122B-A10B-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use AesSedai/Qwen3.5-122B-A10B-GGUF with Docker Model Runner:
docker model run hf.co/AesSedai/Qwen3.5-122B-A10B-GGUF:Q4_K_M
- Lemonade
How to use AesSedai/Qwen3.5-122B-A10B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull AesSedai/Qwen3.5-122B-A10B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Qwen3.5-122B-A10B-GGUF-Q4_K_M
List all available models
lemonade list
Updates
- 5/18/2026: I've uploaded new quants that include the MTP Tensors (@ Q8_0).
- 3/10/2026: I've uploaded new quants using the new fused Up + Gate conversion, this offers up to a +10% boost in prompt processing speed from my testing.
Description
This repo contains specialized MoE-quants for Qwen3.5-122B-A10B. The idea being that given the huge size of the FFN tensors compared to the rest of the tensors in the model, it should be possible to achieve a better quality while keeping the overall size of the entire model smaller compared to a similar naive quantization. To that end, the quantization type default is kept in high quality and the FFN UP + FFN GATE tensors are quanted down along with the FFN DOWN tensors.
| Quant | Size | Mixture | PPL | 1-(Mean PPL(Q)/PPL(base)) | KLD |
|---|---|---|---|---|---|
| Q8_0 | 123.44 GiB (8.51 BPW) | Q8_0 | 4.817202 ยฑ 0.028383 | +0.0132% | 0.003675 ยฑ 0.000034 |
| Q5_K_M | 87.74 GiB (6.05 BPW) | Q8_0 / Q5_K / Q5_K / Q6_K | 4.823697 ยฑ 0.028442 | +0.1481% | 0.005549 ยฑ 0.000042 |
| Q4_K_M | 73.96 GiB (5.10 BPW) | Q8_0 / Q4_K / Q4_K / Q5_K | 4.829970 ยฑ 0.028461 | +0.2783% | 0.010420 ยฑ 0.000078 |
| IQ4_XS | 58.77 GiB (4.05 BPW) | Q8_0 / IQ3_S / IQ3_S / IQ4_XS | 4.914083 ยฑ 0.028949 | +2.0246% | 0.027803 ยฑ 0.000206 |
| IQ3_S | 45.87 GiB (3.16 BPW) | Q6_K / IQ2_S / IQ2_S / IQ3_S | 5.128301 ยฑ 0.030530 | +6.4722% | 0.074536 ยฑ 0.000521 |
| IQ2_XXS | 34.08 GiB (2.35 BPW) | Q4_K / IQ2_XXS / IQ2_XXS / IQ2_XXS | 5.733499 ยฑ 0.035073 | +19.0371% | 0.185455 ยฑ 0.001112 |
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Model tree for AesSedai/Qwen3.5-122B-A10B-GGUF
Base model
Qwen/Qwen3.5-122B-A10B
