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

Libraries

How to use rombodawg/Everyone-LLM-7b-Base-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="rombodawg/Everyone-LLM-7b-Base-GGUF",
	filename="EveryoneLLM-7b.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use rombodawg/Everyone-LLM-7b-Base-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf rombodawg/Everyone-LLM-7b-Base-GGUF
# Run inference directly in the terminal:
llama-cli -hf rombodawg/Everyone-LLM-7b-Base-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf rombodawg/Everyone-LLM-7b-Base-GGUF
# Run inference directly in the terminal:
llama-cli -hf rombodawg/Everyone-LLM-7b-Base-GGUF

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 rombodawg/Everyone-LLM-7b-Base-GGUF
# Run inference directly in the terminal:
./llama-cli -hf rombodawg/Everyone-LLM-7b-Base-GGUF

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 rombodawg/Everyone-LLM-7b-Base-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf rombodawg/Everyone-LLM-7b-Base-GGUF

Use Docker

docker model run hf.co/rombodawg/Everyone-LLM-7b-Base-GGUF

LM Studio
Jan
Ollama
How to use rombodawg/Everyone-LLM-7b-Base-GGUF with Ollama:
```
ollama run hf.co/rombodawg/Everyone-LLM-7b-Base-GGUF
```

Unsloth Studio new

How to use rombodawg/Everyone-LLM-7b-Base-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 rombodawg/Everyone-LLM-7b-Base-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 rombodawg/Everyone-LLM-7b-Base-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

Docker Model Runner
How to use rombodawg/Everyone-LLM-7b-Base-GGUF with Docker Model Runner:
```
docker model run hf.co/rombodawg/Everyone-LLM-7b-Base-GGUF
```

Lemonade

How to use rombodawg/Everyone-LLM-7b-Base-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull rombodawg/Everyone-LLM-7b-Base-GGUF

Run and chat with the model

lemonade run user.Everyone-LLM-7b-Base-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Everyone-LLM-7b-Base-GGUF

EveryoneLLM series of models made by the community, for the community.

This is the first version of Everyone-LLM, a model that combines the power of the large majority of powerfull fine-tuned LLM's made by the community, to create a vast and knowledgable LLM with various abilities.

Prompt template: Alpaca

Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:

The models that were used in this merger were as follow:

Thank you to the creators of the above ai models, they have full credit for the EveryoneLLM series of models. Without their hard work we wouldnt be able to achieve the great success we have in the open source community. 💗

You can find the write up for merging models here:

https://docs.google.com/document/d/1_vOftBnrk9NRk5h10UqrfJ5CDih9KBKL61yvrZtVWPE/edit?usp=sharing

Open LLM Leaderboard Scores

| Model                              | Average |   ARC   | HellaSwag |   MMLU  | TruthfulQA | Winogrande |  GSM8K  |
|------------------------------------|---------|---------|-----------|---------|------------|------------|---------|
|   rombodawg/Everyone-LLM-7b-Base   | 70.21   | 66.38   | 86.02     | 64.94   | 57.89      | 80.43      | 65.58   |

Config for the merger can be found bellow:

models:
  - model: cognitivecomputations_dolphin-2.6-mistral-7b-dpo
    parameters:
      weight: 1
  - model: jondurbin_bagel-dpo-7b-v0.4
    parameters:
      weight: 1
  - model: Locutusque_Hercules-2.0-Mistral-7B
    parameters:
      weight: 1
  - model: Open-Orca_Mistral-7B-OpenOrca
    parameters:
      weight: 1
  - model: teknium_OpenHermes-2.5-Mistral-7B
    parameters:
      weight: 1
  - model: NousResearch_Nous-Capybara-7B-V1.9
    parameters:
      weight: 1
  - model: Intel_neural-chat-7b-v3-3
    parameters:
      weight: 1
  - model: mistralai_Mistral-7B-Instruct-v0.2
    parameters:
      weight: 1
  - model: senseable_WestLake-7B-v2
    parameters:
      weight: 1
  - model: defog_sqlcoder-7b
    parameters:
      weight: 1
  - model: meta-math_MetaMath-Mistral-7B
    parameters:
      weight: 1
  - model: nextai-team_apollo-v1-7b
    parameters:
      weight: 1
  - model: WizardLM_WizardMath-7B-V1.1
    parameters:
      weight: 1
  - model: openchat_openchat-3.5-0106
    parameters:
      weight: 1
merge_method: task_arithmetic
base_model: mistralai_Mistral-7B-v0.1
parameters:
  normalize: true
  int8_mask: true
dtype: float16

Downloads last month: 9

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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