Instructions to use GPT4All-Community/Replete-LLM-Qwen2-7b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GPT4All-Community/Replete-LLM-Qwen2-7b-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="GPT4All-Community/Replete-LLM-Qwen2-7b-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("GPT4All-Community/Replete-LLM-Qwen2-7b-GGUF", dtype="auto")

llama-cpp-python

How to use GPT4All-Community/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="GPT4All-Community/Replete-LLM-Qwen2-7b-GGUF",
	filename="Replete-LLM-Qwen2-7b-Q4_0.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 GPT4All-Community/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 GPT4All-Community/Replete-LLM-Qwen2-7b-GGUF:Q4_0
# Run inference directly in the terminal:
llama-cli -hf GPT4All-Community/Replete-LLM-Qwen2-7b-GGUF:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf GPT4All-Community/Replete-LLM-Qwen2-7b-GGUF:Q4_0
# Run inference directly in the terminal:
llama-cli -hf GPT4All-Community/Replete-LLM-Qwen2-7b-GGUF:Q4_0

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 GPT4All-Community/Replete-LLM-Qwen2-7b-GGUF:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf GPT4All-Community/Replete-LLM-Qwen2-7b-GGUF:Q4_0

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 GPT4All-Community/Replete-LLM-Qwen2-7b-GGUF:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf GPT4All-Community/Replete-LLM-Qwen2-7b-GGUF:Q4_0

Use Docker

docker model run hf.co/GPT4All-Community/Replete-LLM-Qwen2-7b-GGUF:Q4_0

LM Studio
Jan

vLLM

How to use GPT4All-Community/Replete-LLM-Qwen2-7b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GPT4All-Community/Replete-LLM-Qwen2-7b-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": "GPT4All-Community/Replete-LLM-Qwen2-7b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GPT4All-Community/Replete-LLM-Qwen2-7b-GGUF:Q4_0

SGLang

How to use GPT4All-Community/Replete-LLM-Qwen2-7b-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "GPT4All-Community/Replete-LLM-Qwen2-7b-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GPT4All-Community/Replete-LLM-Qwen2-7b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "GPT4All-Community/Replete-LLM-Qwen2-7b-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GPT4All-Community/Replete-LLM-Qwen2-7b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use GPT4All-Community/Replete-LLM-Qwen2-7b-GGUF with Ollama:
```
ollama run hf.co/GPT4All-Community/Replete-LLM-Qwen2-7b-GGUF:Q4_0
```

Unsloth Studio

How to use GPT4All-Community/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 GPT4All-Community/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 GPT4All-Community/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 GPT4All-Community/Replete-LLM-Qwen2-7b-GGUF to start chatting

Docker Model Runner
How to use GPT4All-Community/Replete-LLM-Qwen2-7b-GGUF with Docker Model Runner:
```
docker model run hf.co/GPT4All-Community/Replete-LLM-Qwen2-7b-GGUF:Q4_0
```

Lemonade

How to use GPT4All-Community/Replete-LLM-Qwen2-7b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull GPT4All-Community/Replete-LLM-Qwen2-7b-GGUF:Q4_0

Run and chat with the model

lemonade run user.Replete-LLM-Qwen2-7b-GGUF-Q4_0

List all available models

lemonade list

This is a model that is assumed to perform well, but may require more testing and user feedback. Be aware, only models featured within the GUI of GPT4All, are curated and officially supported by Nomic. Use at your own risk.

About

Static quants of https://huggingface.co/Replete-AI/Replete-LLM-Qwen2-7b at commit e356943
Quantized by ThiloteE with llama.cpp commit e09a800

These quants were created with a customized configuration that have been proven to not cause visible end of string (eos) tokens during inference with GPT4All. The config.json, generation_config.json and tokenizer_config.json differ from the original configuration as can be found in the original model's repository at the time of creation of these quants.

Prompt Template (for GPT4All)

Example System Prompt:

<|im_start|>system
Below is an instruction that describes a task. Write a response that appropriately completes the request.<|im_end|>

Chat Template:

<|im_start|>user
%1<|im_end|>
<|im_start|>assistant
%2<|im_end|>

Context Length

32768

Use a lower value during inference, if you do not have enough RAM or VRAM.

Provided Quants

Link	Type	Size/GB	Notes
GGUF	Q4_0	5.44	fast, recommended

About GGUF

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.

Here is a handy graph by ikawrakow comparing some quant types (lower is better):

And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

Thanks

I thank Mradermacher and TheBloke for Inspiration to this model card and their contributions to open source. Also 3Simplex for lots of help along the way. Shoutout to the GPT4All and llama.cpp communities :-)

Original Model card:

license: apache-2.0 base_model: - Qwen/Qwen2-7B datasets: - Replete-AI/Everything_Instruct_8k_context_filtered tags: - unsloth language: - en

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: 97

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit