Instructions to use Quant-Cartel/SorcererLM-22B-iMat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Quant-Cartel/SorcererLM-22B-iMat-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Quant-Cartel/SorcererLM-22B-iMat-GGUF",
	filename="SorcererLM-22B-iMat-IQ1_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Quant-Cartel/SorcererLM-22B-iMat-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Quant-Cartel/SorcererLM-22B-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Quant-Cartel/SorcererLM-22B-iMat-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 Quant-Cartel/SorcererLM-22B-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Quant-Cartel/SorcererLM-22B-iMat-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 Quant-Cartel/SorcererLM-22B-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Quant-Cartel/SorcererLM-22B-iMat-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 Quant-Cartel/SorcererLM-22B-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Quant-Cartel/SorcererLM-22B-iMat-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Quant-Cartel/SorcererLM-22B-iMat-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Quant-Cartel/SorcererLM-22B-iMat-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Quant-Cartel/SorcererLM-22B-iMat-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": "Quant-Cartel/SorcererLM-22B-iMat-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Quant-Cartel/SorcererLM-22B-iMat-GGUF:Q4_K_M

Ollama
How to use Quant-Cartel/SorcererLM-22B-iMat-GGUF with Ollama:
```
ollama run hf.co/Quant-Cartel/SorcererLM-22B-iMat-GGUF:Q4_K_M
```

Unsloth Studio

How to use Quant-Cartel/SorcererLM-22B-iMat-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 Quant-Cartel/SorcererLM-22B-iMat-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 Quant-Cartel/SorcererLM-22B-iMat-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Quant-Cartel/SorcererLM-22B-iMat-GGUF to start chatting

Docker Model Runner
How to use Quant-Cartel/SorcererLM-22B-iMat-GGUF with Docker Model Runner:
```
docker model run hf.co/Quant-Cartel/SorcererLM-22B-iMat-GGUF:Q4_K_M
```

Lemonade

How to use Quant-Cartel/SorcererLM-22B-iMat-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Quant-Cartel/SorcererLM-22B-iMat-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.SorcererLM-22B-iMat-GGUF-Q4_K_M

List all available models

lemonade list

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PROUDLY PRESENTS

SorcererLM-22B-iMat-GGUF

Quantized with love from fp32.

Importance Matrix calculated using groups_merged.txt
107 chunks
n_ctx=512
Importance Matrix uses fp32 precision model weights, fp32.imatrix file to be added in repo

Original model README here and below:

SorcererLM-22B

Because good things always come in threes!

SorcererLM-22B is here, rounding out the trinity of Mistral-Small-Instruct tunes from the Quant Cartel.

Prompt Format

Prompt Template: Mistral V2 & V3 Context / Instruct Templates
Samplers / Advanced Instruct Template: See Quant-Cartel/Recommended-Settings/SorcererLM-22B

Quantized Versions

Training

For starters this is a LORA tune on top of Mistral-Small-Instruct-2409 and not a pruned version of SorcererLM-8x22b.

Trained with a whole lot of love on 1 epoch of cleaned and deduped c2 logs. This model is 100% 'born-local', the result of roughly 27 hours and a little bit of patience on a single RTX 4080 SUPER.

As hyperparameters and dataset intentionally mirror ones used in the original Sorcerer 8x22b tune, this is considered its 'lite' counterpart aiming to provide the same bespoke conversational experience relative to its size and reduced hardware requirements.

While all three share the same Mistral-Small-Instruct base, in contrast to its sisters Mistral-Small-NovusKyver and Acolyte-22B this release did not SLERP the resulting model with the original in a 50/50 ratio post-training. Instead, alpha was dropped when the lora was merged with full precision weights in the final step.

Acknowledgments

First and foremost a huge thank you my brilliant teammates envoid and rAIfle. Special shout-out to rAIfle for critical last minute advice that got this one through the finish line
Props to unsloth as well for helping make this local tune possible
And of course, none of this would matter without users like you. Thank you :)

Safety

...

Downloads last month: 529

GGUF

Model size

22B params

Architecture

llama

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for Quant-Cartel/SorcererLM-22B-iMat-GGUF

Base model

InferenceIllusionist/SorcererLM-22B

Quantized

(6)

this model