Instructions to use mradermacher/shisa-v2.1-qwen3-8b-i1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/shisa-v2.1-qwen3-8b-i1-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/shisa-v2.1-qwen3-8b-i1-GGUF", dtype="auto")

llama-cpp-python

How to use mradermacher/shisa-v2.1-qwen3-8b-i1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="mradermacher/shisa-v2.1-qwen3-8b-i1-GGUF",
	filename="shisa-v2.1-qwen3-8b.i1-IQ1_M.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 mradermacher/shisa-v2.1-qwen3-8b-i1-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mradermacher/shisa-v2.1-qwen3-8b-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf mradermacher/shisa-v2.1-qwen3-8b-i1-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 mradermacher/shisa-v2.1-qwen3-8b-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf mradermacher/shisa-v2.1-qwen3-8b-i1-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 mradermacher/shisa-v2.1-qwen3-8b-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/shisa-v2.1-qwen3-8b-i1-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 mradermacher/shisa-v2.1-qwen3-8b-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/shisa-v2.1-qwen3-8b-i1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mradermacher/shisa-v2.1-qwen3-8b-i1-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mradermacher/shisa-v2.1-qwen3-8b-i1-GGUF with Ollama:
```
ollama run hf.co/mradermacher/shisa-v2.1-qwen3-8b-i1-GGUF:Q4_K_M
```

Unsloth Studio

How to use mradermacher/shisa-v2.1-qwen3-8b-i1-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 mradermacher/shisa-v2.1-qwen3-8b-i1-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 mradermacher/shisa-v2.1-qwen3-8b-i1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mradermacher/shisa-v2.1-qwen3-8b-i1-GGUF to start chatting

How to use mradermacher/shisa-v2.1-qwen3-8b-i1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf mradermacher/shisa-v2.1-qwen3-8b-i1-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": "mradermacher/shisa-v2.1-qwen3-8b-i1-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mradermacher/shisa-v2.1-qwen3-8b-i1-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 mradermacher/shisa-v2.1-qwen3-8b-i1-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 mradermacher/shisa-v2.1-qwen3-8b-i1-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use mradermacher/shisa-v2.1-qwen3-8b-i1-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/shisa-v2.1-qwen3-8b-i1-GGUF:Q4_K_M
```

Lemonade

How to use mradermacher/shisa-v2.1-qwen3-8b-i1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/shisa-v2.1-qwen3-8b-i1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.shisa-v2.1-qwen3-8b-i1-GGUF-Q4_K_M

List all available models

lemonade list

mradermacher commited on Dec 9, 2025

Commit

3739d1b

verified ·

1 Parent(s): 058ab6b

auto-patch README.md

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@@ -1,3 +1,24 @@
 <!-- ### quantize_version: 2 -->
 <!-- ### output_tensor_quantised: 1 -->
 <!-- ### convert_type: hf -->
@@ -7,3 +28,43 @@
 <!-- ### quants_skip:  -->
 <!-- ### skip_mmproj:  -->
 weighted/imatrix quants of https://huggingface.co/shisa-ai/shisa-v2.1-qwen3-8b

+---
+base_model: shisa-ai/shisa-v2.1-qwen3-8b
+datasets:
+- shisa-ai/shisa-v2.1-sharegpt
+language:
+- ja
+- en
+library_name: transformers
+license: apache-2.0
+model_name: Llama 3.2 Shisa V2.1 3B
+mradermacher:
+  readme_rev: 1
+quantized_by: mradermacher
+tags:
+- shisa
+- qwen
+- axolotl
+- trl
+---
+## About
 <!-- ### quantize_version: 2 -->
 <!-- ### output_tensor_quantised: 1 -->
 <!-- ### convert_type: hf -->
 <!-- ### quants_skip:  -->
 <!-- ### skip_mmproj:  -->
 weighted/imatrix quants of https://huggingface.co/shisa-ai/shisa-v2.1-qwen3-8b
+<!-- provided-files -->
+***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#shisa-v2.1-qwen3-8b-i1-GGUF).***
+static quants are available at https://huggingface.co/mradermacher/shisa-v2.1-qwen3-8b-GGUF
+## Usage
+If you are unsure how to use GGUF files, refer to one of [TheBloke's
+READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
+more details, including on how to concatenate multi-part files.
+## Provided Quants
+(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
+| Link | Type | Size/GB | Notes |
+|:-----|:-----|--------:|:------|
+| [GGUF](https://huggingface.co/mradermacher/shisa-v2.1-qwen3-8b-i1-GGUF/resolve/main/shisa-v2.1-qwen3-8b.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own quants) |
+Here is a handy graph by ikawrakow comparing some lower-quality quant
+types (lower is better):
+![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)
+And here are Artefact2's thoughts on the matter:
+https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
+## FAQ / Model Request
+See https://huggingface.co/mradermacher/model_requests for some answers to
+questions you might have and/or if you want some other model quantized.
+## Thanks
+I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
+me use its servers and providing upgrades to my workstation to enable
+this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
+<!-- end -->