Instructions to use Quant-Cartel/experiment_2_8b-iMat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Quant-Cartel/experiment_2_8b-iMat-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Quant-Cartel/experiment_2_8b-iMat-GGUF",
	filename="experiment_2_8b-iMat-IQ2_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Quant-Cartel/experiment_2_8b-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/experiment_2_8b-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Quant-Cartel/experiment_2_8b-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/experiment_2_8b-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Quant-Cartel/experiment_2_8b-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/experiment_2_8b-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Quant-Cartel/experiment_2_8b-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/experiment_2_8b-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Quant-Cartel/experiment_2_8b-iMat-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Quant-Cartel/experiment_2_8b-iMat-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use Quant-Cartel/experiment_2_8b-iMat-GGUF with Ollama:
```
ollama run hf.co/Quant-Cartel/experiment_2_8b-iMat-GGUF:Q4_K_M
```

Unsloth Studio new

How to use Quant-Cartel/experiment_2_8b-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/experiment_2_8b-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/experiment_2_8b-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/experiment_2_8b-iMat-GGUF to start chatting

Docker Model Runner
How to use Quant-Cartel/experiment_2_8b-iMat-GGUF with Docker Model Runner:
```
docker model run hf.co/Quant-Cartel/experiment_2_8b-iMat-GGUF:Q4_K_M
```

Lemonade

How to use Quant-Cartel/experiment_2_8b-iMat-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Quant-Cartel/experiment_2_8b-iMat-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.experiment_2_8b-iMat-GGUF-Q4_K_M

List all available models

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PROUDLY PRESENTS

experiment_2_8b-iMat-GGUF

Quantization Notes: Quantized from 3500 checkpoint. Use repetition penalty (--repeat-penalty on llama.cpp) of ~1.15 with Q6_K and lower and ~1.18 with IQ3_M and lower for best results.

Quantized from fp16 with love.

Weighted quantizations were created using fp16 GGUF and groups_merged-enhancedV2-TurboMini.txt in 189 chunks and n_ctx=512
This method of calculating the importance matrix showed improvements in some areas for Mistral 7b and Llama3 8b models, see above post for details
The enhancedv2-turbomini file appends snippets from turboderp's calibration data to the standard groups_merged.txt file

For a brief rundown of iMatrix quant performance please see this PR

All quants are verified working prior to uploading to repo for your safety and convenience.

Original model card here and below

experiment_2_8b-fp16

Another experimental train w/ unsloth. This time, roughly 0.6 epochs of the cleaned c2-logs. My metaparams are probably bad, since the loss-value was super weird at the end. Also uploaded another version in the checkpoint-3500-branch that may mitigate some of that.

Downloads last month: 99

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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