Instructions to use InferenceIllusionist/llama3-42b-v0-iMat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use InferenceIllusionist/llama3-42b-v0-iMat-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="InferenceIllusionist/llama3-42b-v0-iMat-GGUF",
	filename="llama3-42b-v0-iMat-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 InferenceIllusionist/llama3-42b-v0-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 InferenceIllusionist/llama3-42b-v0-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf InferenceIllusionist/llama3-42b-v0-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 InferenceIllusionist/llama3-42b-v0-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf InferenceIllusionist/llama3-42b-v0-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 InferenceIllusionist/llama3-42b-v0-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf InferenceIllusionist/llama3-42b-v0-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 InferenceIllusionist/llama3-42b-v0-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf InferenceIllusionist/llama3-42b-v0-iMat-GGUF:Q4_K_M

Use Docker

docker model run hf.co/InferenceIllusionist/llama3-42b-v0-iMat-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use InferenceIllusionist/llama3-42b-v0-iMat-GGUF with Ollama:
```
ollama run hf.co/InferenceIllusionist/llama3-42b-v0-iMat-GGUF:Q4_K_M
```

Unsloth Studio

How to use InferenceIllusionist/llama3-42b-v0-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 InferenceIllusionist/llama3-42b-v0-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 InferenceIllusionist/llama3-42b-v0-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 InferenceIllusionist/llama3-42b-v0-iMat-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use InferenceIllusionist/llama3-42b-v0-iMat-GGUF with Docker Model Runner:
```
docker model run hf.co/InferenceIllusionist/llama3-42b-v0-iMat-GGUF:Q4_K_M
```

Lemonade

How to use InferenceIllusionist/llama3-42b-v0-iMat-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull InferenceIllusionist/llama3-42b-v0-iMat-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.llama3-42b-v0-iMat-GGUF-Q4_K_M

List all available models

lemonade list

llama3-42b-v0-iMat-GGUF

Quantized from fp32 with love. All credits to Charles Goddard for the original model.

Weighted quantizations were calculated using groups_merged.txt with 105 chunks (recommended amount for this file) and n_ctx=512. Special thanks to jukofyork for sharing this process

For more information on the pruning technique utilized in this model: https://arxiv.org/abs/2403.17887

Brief rundown of iMatrix quant performance

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

Tip: Pick a size that can fit in your GPU while still allowing some room for context for best speed. You may need to pad this further depending on if you are running image gen or TTS as well.

FP16 model card can be found here

Downloads last month: 278

GGUF

Model size

43B params

Architecture

llama

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Inference Providers NEW

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Collection including InferenceIllusionist/llama3-42b-v0-iMat-GGUF

GGUFs

Collection

I take requests, feel free to drop me a line in the community posts • 31 items • Updated Apr 28 • 3

Paper for InferenceIllusionist/llama3-42b-v0-iMat-GGUF

The Unreasonable Ineffectiveness of the Deeper Layers

Paper • 2403.17887 • Published Mar 26, 2024 • 83