Instructions to use nisten/meta-405b-instruct-cpu-optimized-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nisten/meta-405b-instruct-cpu-optimized-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="nisten/meta-405b-instruct-cpu-optimized-gguf",
	filename="meta-405b-cpu-i1-q4xs-00001-of-00005.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 nisten/meta-405b-instruct-cpu-optimized-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf nisten/meta-405b-instruct-cpu-optimized-gguf:BF16
# Run inference directly in the terminal:
llama-cli -hf nisten/meta-405b-instruct-cpu-optimized-gguf:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf nisten/meta-405b-instruct-cpu-optimized-gguf:BF16
# Run inference directly in the terminal:
llama-cli -hf nisten/meta-405b-instruct-cpu-optimized-gguf:BF16

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 nisten/meta-405b-instruct-cpu-optimized-gguf:BF16
# Run inference directly in the terminal:
./llama-cli -hf nisten/meta-405b-instruct-cpu-optimized-gguf:BF16

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 nisten/meta-405b-instruct-cpu-optimized-gguf:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nisten/meta-405b-instruct-cpu-optimized-gguf:BF16

Use Docker

docker model run hf.co/nisten/meta-405b-instruct-cpu-optimized-gguf:BF16

LM Studio
Jan
Ollama
How to use nisten/meta-405b-instruct-cpu-optimized-gguf with Ollama:
```
ollama run hf.co/nisten/meta-405b-instruct-cpu-optimized-gguf:BF16
```

Unsloth Studio new

How to use nisten/meta-405b-instruct-cpu-optimized-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 nisten/meta-405b-instruct-cpu-optimized-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 nisten/meta-405b-instruct-cpu-optimized-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for nisten/meta-405b-instruct-cpu-optimized-gguf to start chatting

Docker Model Runner
How to use nisten/meta-405b-instruct-cpu-optimized-gguf with Docker Model Runner:
```
docker model run hf.co/nisten/meta-405b-instruct-cpu-optimized-gguf:BF16
```

Lemonade

How to use nisten/meta-405b-instruct-cpu-optimized-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nisten/meta-405b-instruct-cpu-optimized-gguf:BF16

Run and chat with the model

lemonade run user.meta-405b-instruct-cpu-optimized-gguf-BF16

List all available models

lemonade list

How to use from the

Use from the

llama-cpp-python library

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="nisten/meta-405b-instruct-cpu-optimized-gguf",
	filename="",
)

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

🚀 CPU optimized quantizations of Meta-Llama-3.1-405B-Instruct 🖥️

This repository contains CPU-optimized GGUF quantizations of the Meta-Llama-3.1-405B-Instruct model. These quantizations are designed to run efficiently on CPU hardware while maintaining good performance.

Available Quantizations

Q4_0_4_8 (CPU FMA-Optimized): ~246 GB
IQ4_XS (Fastest for CPU/GPU): ~212 GB
Q2K-Q8 Mixed quant with iMatrix: ~154 GB
Q2K-Q8 Mixed without iMat for testing: ~165 GB
1-bit Custom per weight COHERENT quant: ~103 GB
BF16: ~811 GB (original model)
Q8_0: ~406 GB (original model)

Use Aria2 for parallelized downloads, links will download 9x faster

🐧 On Linux sudo apt install -y aria2

🍎 On Mac brew install aria2

Feel free to paste these all in at once or one at a time

Q4_0_48 (CPU FMA Optimized Specifically for ARM server chips, NOT TESTED on X86)

aria2c -x 16 -s 16 -k 1M -o meta-405b-inst-cpu-optimized-q4048-00001-of-00006.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-405b-inst-cpu-optimized-q4048-00001-of-00006.gguf
aria2c -x 16 -s 16 -k 1M -o meta-405b-inst-cpu-optimized-q4048-00002-of-00006.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-405b-inst-cpu-optimized-q4048-00002-of-00006.gguf
aria2c -x 16 -s 16 -k 1M -o meta-405b-inst-cpu-optimized-q4048-00003-of-00006.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-405b-inst-cpu-optimized-q4048-00003-of-00006.gguf
aria2c -x 16 -s 16 -k 1M -o meta-405b-inst-cpu-optimized-q4048-00004-of-00006.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-405b-inst-cpu-optimized-q4048-00004-of-00006.gguf
aria2c -x 16 -s 16 -k 1M -o meta-405b-inst-cpu-optimized-q4048-00005-of-00006.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-405b-inst-cpu-optimized-q4048-00005-of-00006.gguf
aria2c -x 16 -s 16 -k 1M -o meta-405b-inst-cpu-optimized-q4048-00006-of-00006.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-405b-inst-cpu-optimized-q4048-00006-of-00006.gguf

IQ4_XS Version - Fastest for CPU/GPU should work everywhere (Size: ~212 GB)

aria2c -x 16 -s 16 -k 1M -o meta-405b-cpu-i1-q4xs-00001-of-00005.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-405b-cpu-i1-q4xs-00001-of-00005.gguf
aria2c -x 16 -s 16 -k 1M -o meta-405b-cpu-i1-q4xs-00002-of-00005.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-405b-cpu-i1-q4xs-00002-of-00005.gguf
aria2c -x 16 -s 16 -k 1M -o meta-405b-cpu-i1-q4xs-00003-of-00005.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-405b-cpu-i1-q4xs-00003-of-00005.gguf
aria2c -x 16 -s 16 -k 1M -o meta-405b-cpu-i1-q4xs-00004-of-00005.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-405b-cpu-i1-q4xs-00004-of-00005.gguf
aria2c -x 16 -s 16 -k 1M -o meta-405b-cpu-i1-q4xs-00005-of-00005.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-405b-cpu-i1-q4xs-00005-of-00005.gguf

1-bit Custom Per Weight Quantization (Size: ~103 GB)

aria2c -x 16 -s 16 -k 1M -o meta-405b-1bit-00001-of-00003.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-405b-1bit-00001-of-00003.gguf
aria2c -x 16 -s 16 -k 1M -o meta-405b-1bit-00002-of-00003.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-405b-1bit-00002-of-00003.gguf
aria2c -x 16 -s 16 -k 1M -o meta-405b-1bit-00003-of-00003.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-405b-1bit-00003-of-00003.gguf

Q2K-Q8 Mixed 2bit 8bit I wrote myself. This is the smallest coherent one I could make WITHOUT imatrix

aria2c -x 16 -s 16 -k 1M -o meta-405b-inst-cpu-2kmix8k-00001-of-00004.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-405b-inst-cpu-2kmix8k-00001-of-00004.gguf
aria2c -x 16 -s 16 -k 1M -o meta-405b-inst-cpu-2kmix8k-00002-of-00004.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-405b-inst-cpu-2kmix8k-00002-of-00004.gguf
aria2c -x 16 -s 16 -k 1M -o meta-405b-inst-cpu-2kmix8k-00003-of-00004.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-405b-inst-cpu-2kmix8k-00003-of-00004.gguf
aria2c -x 16 -s 16 -k 1M -o meta-405b-inst-cpu-2kmix8k-00004-of-00004.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-405b-inst-cpu-2kmix8k-00004-of-00004.gguf

Same as above but with higher quality iMatrix Q2K-Q8 (Size: ~154 GB) USE THIS ONE

aria2c -x 16 -s 16 -k 1M -o meta-405b-cpu-imatrix-2k-00001-of-00004.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-405b-cpu-imatrix-2k-00001-of-00004.gguf
aria2c -x 16 -s 16 -k 1M -o meta-405b-cpu-imatrix-2k-00002-of-00004.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-405b-cpu-imatrix-2k-00002-of-00004.gguf
aria2c -x 16 -s 16 -k 1M -o meta-405b-cpu-imatrix-2k-00003-of-00004.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-405b-cpu-imatrix-2k-00003-of-00004.gguf
aria2c -x 16 -s 16 -k 1M -o meta-405b-cpu-imatrix-2k-00004-of-00004.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-405b-cpu-imatrix-2k-00004-of-00004.gguf

Q4_0_48 CPU Optimized example response — **Q4_0_48 (CPU Optimized) (246GB):** Example response of 20000 token prompt

BF16 Version

aria2c -x 16 -s 16 -k 1M -o meta-llama-405b-inst-bf16-00001-of-00019.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-llama-405b-inst-bf16-00001-of-00019.gguf
aria2c -x 16 -s 16 -k 1M -o meta-llama-405b-inst-bf16-00002-of-00019.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-llama-405b-inst-bf16-00002-of-00019.gguf
aria2c -x 16 -s 16 -k 1M -o meta-llama-405b-inst-bf16-00003-of-00019.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-llama-405b-inst-bf16-00003-of-00019.gguf
aria2c -x 16 -s 16 -k 1M -o meta-llama-405b-inst-bf16-00004-of-00019.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-llama-405b-inst-bf16-00004-of-00019.gguf
aria2c -x 16 -s 16 -k 1M -o meta-llama-405b-inst-bf16-00005-of-00019.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-llama-405b-inst-bf16-00005-of-00019.gguf
aria2c -x 16 -s 16 -k 1M -o meta-llama-405b-inst-bf16-00006-of-00019.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-llama-405b-inst-bf16-00006-of-00019.gguf
aria2c -x 16 -s 16 -k 1M -o meta-llama-405b-inst-bf16-00007-of-00019.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-llama-405b-inst-bf16-00007-of-00019.gguf
aria2c -x 16 -s 16 -k 1M -o meta-llama-405b-inst-bf16-00008-of-00019.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-llama-405b-inst-bf16-00008-of-00019.gguf
aria2c -x 16 -s 16 -k 1M -o meta-llama-405b-inst-bf16-00009-of-00019.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-llama-405b-inst-bf16-00009-of-00019.gguf
aria2c -x 16 -s 16 -k 1M -o meta-llama-405b-inst-bf16-00010-of-00019.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-llama-405b-inst-bf16-00010-of-00019.gguf
aria2c -x 16 -s 16 -k 1M -o meta-llama-405b-inst-bf16-00011-of-00019.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-llama-405b-inst-bf16-00011-of-00019.gguf
aria2c -x 16 -s 16 -k 1M -o meta-llama-405b-inst-bf16-00012-of-00019.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-llama-405b-inst-bf16-00012-of-00019.gguf
aria2c -x 16 -s 16 -k 1M -o meta-llama-405b-inst-bf16-00013-of-00019.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-llama-405b-inst-bf16-00013-of-00019.gguf
aria2c -x 16 -s 16 -k 1M -o meta-llama-405b-inst-bf16-00014-of-00019.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-llama-405b-inst-bf16-00014-of-00019.gguf
aria2c -x 16 -s 16 -k 1M -o meta-llama-405b-inst-bf16-00015-of-00019.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-llama-405b-inst-bf16-00015-of-00019.gguf
aria2c -x 16 -s 16 -k 1M -o meta-llama-405b-inst-bf16-00016-of-00019.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-llama-405b-inst-bf16-00016-of-00019.gguf
aria2c -x 16 -s 16 -k 1M -o meta-llama-405b-inst-bf16-00017-of-00019.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-llama-405b-inst-bf16-00017-of-00019.gguf
aria2c -x 16 -s 16 -k 1M -o meta-llama-405b-inst-bf16-00018-of-00019.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-llama-405b-inst-bf16-00018-of-00019.gguf
aria2c -x 16 -s 16 -k 1M -o meta-llama-405b-inst-bf16-00019-of-00019.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-llama-405b-inst-bf16-00019-of-00019.gguf

Q8_0 Version

aria2c -x 16 -s 16 -k 1M -o meta-llama-405b-inst-q8_0-00001-of-00010.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-llama-405b-inst-q8_0-00001-of-00010.gguf
aria2c -x 16 -s 16 -k 1M -o meta-llama-405b-inst-q8_0-00002-of-00010.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-llama-405b-inst-q8_0-00002-of-00010.gguf
aria2c -x 16 -s 16 -k 1M -o meta-llama-405b-inst-q8_0-00003-of-00010.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-llama-405b-inst-q8_0-00003-of-00010.gguf
aria2c -x 16 -s 16 -k 1M -o meta-llama-405b-inst-q8_0-00004-of-00010.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-llama-405b-inst-q8_0-00004-of-00010.gguf
aria2c -x 16 -s 16 -k 1M -o meta-llama-405b-inst-q8_0-00005-of-00010.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-llama-405b-inst-q8_0-00005-of-00010.gguf
aria2c -x 16 -s 16 -k 1M -o meta-llama-405b-inst-q8_0-00006-of-00010.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-llama-405b-inst-q8_0-00006-of-00010.gguf
aria2c -x 16 -s 16 -k 1M -o meta-llama-405b-inst-q8_0-00007-of-00010.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-llama-405b-inst-q8_0-00007-of-00010.gguf
aria2c -x 16 -s 16 -k 1M -o meta-llama-405b-inst-q8_0-00008-of-00010.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-llama-405b-inst-q8_0-00008-of-00010.gguf
aria2c -x 16 -s 16 -k 1M -o meta-llama-405b-inst-q8_0-00009-of-00010.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-llama-405b-inst-q8_0-00009-of-00010.gguf
aria2c -x 16 -s 16 -k 1M -o meta-llama-405b-inst-q8_0-00010-of-00010.gguf https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/resolve/main/meta-llama-405b-inst-q8_0-00010-of-00010.gguf

Usage

After downloading, you can use these models with libraries like llama.cpp. Here's a basic example:

 ./llama-cli -t 32 --temp 0.4 -fa -m ~/meow/meta-405b-inst-cpu-optimized-q4048-00001-of-00006.gguf -b 512 -c 9000 -p "Adopt the persona of a NASA JPL mathmatician and firendly helpful programmer." -cnv -co -i

Model Information

This model is based on the Meta-Llama-3.1-405B-Instruct model. It's an instruction-tuned version of the 405B parameter Llama 3.1 model, designed for assistant-like chat and various natural language generation tasks.

Key features:

405 billion parameters
Supports 8 languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai
128k context length
Uses Grouped-Query Attention (GQA) for improved inference scalability

For more detailed information about the base model, please refer to the original model card.

License

The use of this model is subject to the Llama 3.1 Community License. Please ensure you comply with the license terms when using this model.

Acknowledgements

Special thanks to the Meta AI team for creating and releasing the Llama 3.1 model series.

Enjoy; more quants and perplexity benchmarks coming.

Downloads last month: 107

GGUF

Model size

410B params

Architecture

llama

Hardware compatibility

8-bit

16-bit

View +4 variants

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Model tree for nisten/meta-405b-instruct-cpu-optimized-gguf

Base model

meta-llama/Llama-3.1-405B

Finetuned

meta-llama/Llama-3.1-405B-Instruct

Quantized

(30)

this model