---

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

---

![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)

# QuantFactory/Replete-LLM-Qwen2-7b-GGUF
This is quantized version of [Replete-AI/Replete-LLM-Qwen2-7b](https://huggingface.co/Replete-AI/Replete-LLM-Qwen2-7b) created using llama.cpp

# Original Model Card

Replete-LLM-Qwen2-7b

![image/png](https://cdn-uploads.huggingface.co/production/uploads/642cc1c253e76b4c2286c58e/q9gC-_O4huL2pK4nY-Y2x.png)

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:

- https://huggingface.co/bartowski/Replete-LLM-Qwen2-7b-exl2

- https://huggingface.co/bartowski/Replete-LLM-Qwen2-7b-GGUF

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):

![image/png](https://cdn-uploads.huggingface.co/production/uploads/642cc1c253e76b4c2286c58e/75SR21J3-zbTGKYbeoBzX.png)

**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.

```python
# 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