Instructions to use QuantFactory/Replete-LLM-Qwen2-7b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Replete-LLM-Qwen2-7b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Replete-LLM-Qwen2-7b-GGUF",
	filename="Replete-LLM-Qwen2-7b.Q2_K.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 QuantFactory/Replete-LLM-Qwen2-7b-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Replete-LLM-Qwen2-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Replete-LLM-Qwen2-7b-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 QuantFactory/Replete-LLM-Qwen2-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Replete-LLM-Qwen2-7b-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 QuantFactory/Replete-LLM-Qwen2-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Replete-LLM-Qwen2-7b-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 QuantFactory/Replete-LLM-Qwen2-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Replete-LLM-Qwen2-7b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Replete-LLM-Qwen2-7b-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/Replete-LLM-Qwen2-7b-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Replete-LLM-Qwen2-7b-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Replete-LLM-Qwen2-7b-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 QuantFactory/Replete-LLM-Qwen2-7b-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 QuantFactory/Replete-LLM-Qwen2-7b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Replete-LLM-Qwen2-7b-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/Replete-LLM-Qwen2-7b-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Replete-LLM-Qwen2-7b-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Replete-LLM-Qwen2-7b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Replete-LLM-Qwen2-7b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Replete-LLM-Qwen2-7b-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/Replete-LLM-Qwen2-7b-GGUF

This is quantized version of Replete-AI/Replete-LLM-Qwen2-7b created using llama.cpp

Original Model Card

Replete-LLM-Qwen2-7b

Thank you to TensorDock for sponsoring Replete-LLM you can check out their website for cloud compute rental below.

https://tensordock.com

Replete-LLM is Replete-AI's flagship model. We take pride in releasing a fully open-source, low parameter, and competitive AI model that not only surpasses its predecessor Qwen2-7B-Instruct in performance, but also competes with (if not surpasses) other flagship models from closed source like gpt-3.5-turbo, but also open source models such as gemma-2-9b-it and Meta-Llama-3.1-8B-Instruct in terms of overall performance across all fields and categories. You can find the dataset that this model was trained on linked bellow:

https://huggingface.co/datasets/Replete-AI/Everything_Instruct_8k_context_filtered

Try bartowski's quantizations:

Cant run the model locally? Well then use the huggingface space instead:

https://huggingface.co/spaces/rombodawg/Replete-LLM-Qwen2-7b

Some statistics about the data the model was trained on can be found in the image and details bellow, while a more comprehensive look can be found in the model card for the dataset. (linked above):

Replete-LLM-Qwen2-7b is a versatile model fine-tuned to excel on any imaginable task. The following types of generations were included in the fine-tuning process:

Science: (General, Physical Reasoning)
Social Media: (Reddit, Twitter)
General Knowledge: (Character-Codex), (Famous Quotes), (Steam Video Games), (How-To? Explanations)
Cooking: (Cooking Preferences, Recipes)
Writing: (Poetry, Essays, General Writing)
Medicine: (General Medical Data)
History: (General Historical Data)
Law: (Legal Q&A)
Role-Play: (Couple-RP, Roleplay Conversations)
News: (News Generation)
Coding: (3 million rows of coding data in over 100 coding languages)
Math: (Math data from TIGER-Lab/MathInstruct)
Function Calling: (Function calling data from "glaiveai/glaive-function-calling-v2")
General Instruction: (All of teknium/OpenHermes-2.5 fully filtered and uncensored)

Prompt Template: ChatML

<|im_start|>system
{}<|im_end|>
<|im_start|>user
{}<|im_end|>
<|im_start|>assistant
{}

End token (eot_token)

<|endoftext|>

Want to know the secret sause of how this model was made? Find the write up bellow

Continuous Fine-tuning Without Loss Using Lora and Mergekit

https://docs.google.com/document/d/1OjbjU5AOz4Ftn9xHQrX3oFQGhQ6RDUuXQipnQ9gn6tU/edit?usp=sharing

The code to finetune this AI model can be found bellow

https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?usp=sharing
Note this model in particular was finetuned using an h100 using Tensordock.com using the Pytorch OS. In order to use Unsloth code with TensorDock you need to run the following code (Bellow) to reinstall drivers on TensorDock before unsloth works. After running the code bellow, your Virtual Machine will reset, and you will have to SSH back into it. And then you can run the normal unsloth code in order.

# Check Current Size
!df -h /dev/shm

# Increase Size Temporarily
!sudo mount -o remount,size=16G /dev/shm

# Increase Size Permanently
!echo "tmpfs /dev/shm tmpfs defaults,size=16G 0 0" | sudo tee -a /etc/fstab

# Remount /dev/shm
!sudo mount -o remount /dev/shm


# Verify the Changes
!df -h /dev/shm

!nvcc --version

!export TORCH_DISTRIBUTED_DEBUG=DETAIL
!export NCCL_DEBUG=INFO
!python -c "import torch; print(torch.version.cuda)"
!export PATH=/usr/local/cuda/bin:$PATH
!export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
!export NCCL_P2P_LEVEL=NVL
!export NCCL_DEBUG=INFO
!export NCCL_DEBUG_SUBSYS=ALL
!export TORCH_DISTRIBUTED_DEBUG=INFO
!export TORCHELASTIC_ERROR_FILE=/PATH/TO/torcherror.log
!sudo apt-get remove --purge -y '^nvidia-.*'
!sudo apt-get remove --purge -y '^cuda-.*'
!sudo apt-get autoremove -y
!sudo apt-get autoclean -y
!sudo apt-get update -y
!sudo apt-get install -y nvidia-driver-535 cuda-12-1
!sudo add-apt-repository ppa:graphics-drivers/ppa -y
!sudo apt-get update -y
!sudo apt-get update -y
!sudo apt-get install -y software-properties-common
!sudo add-apt-repository ppa:graphics-drivers/ppa -y
!sudo apt-get update -y
!latest_driver=$(apt-cache search '^nvidia-driver-[0-9]' | grep -oP 'nvidia-driver-\K[0-9]+' | sort -n | tail -1) && sudo apt-get install -y nvidia-driver-$latest_driver
!sudo reboot

Join the Replete-Ai discord! We are a great and Loving community!

https://discord.gg/ZZbnsmVnjD

Downloads last month: 99

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for QuantFactory/Replete-LLM-Qwen2-7b-GGUF

Base model

Qwen/Qwen2-7B

Quantized

(44)

this model