Instructions to use RichardErkhov/GritLM_-_GritLM-8x7B-KTO-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RichardErkhov/GritLM_-_GritLM-8x7B-KTO-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="RichardErkhov/GritLM_-_GritLM-8x7B-KTO-gguf",
	filename="GritLM-8x7B-KTO.IQ3_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use RichardErkhov/GritLM_-_GritLM-8x7B-KTO-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf RichardErkhov/GritLM_-_GritLM-8x7B-KTO-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf RichardErkhov/GritLM_-_GritLM-8x7B-KTO-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 RichardErkhov/GritLM_-_GritLM-8x7B-KTO-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf RichardErkhov/GritLM_-_GritLM-8x7B-KTO-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 RichardErkhov/GritLM_-_GritLM-8x7B-KTO-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RichardErkhov/GritLM_-_GritLM-8x7B-KTO-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 RichardErkhov/GritLM_-_GritLM-8x7B-KTO-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RichardErkhov/GritLM_-_GritLM-8x7B-KTO-gguf:Q4_K_M

Use Docker

docker model run hf.co/RichardErkhov/GritLM_-_GritLM-8x7B-KTO-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use RichardErkhov/GritLM_-_GritLM-8x7B-KTO-gguf with Ollama:
```
ollama run hf.co/RichardErkhov/GritLM_-_GritLM-8x7B-KTO-gguf:Q4_K_M
```

Unsloth Studio

How to use RichardErkhov/GritLM_-_GritLM-8x7B-KTO-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 RichardErkhov/GritLM_-_GritLM-8x7B-KTO-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 RichardErkhov/GritLM_-_GritLM-8x7B-KTO-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for RichardErkhov/GritLM_-_GritLM-8x7B-KTO-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use RichardErkhov/GritLM_-_GritLM-8x7B-KTO-gguf with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/GritLM_-_GritLM-8x7B-KTO-gguf:Q4_K_M
```

Lemonade

How to use RichardErkhov/GritLM_-_GritLM-8x7B-KTO-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RichardErkhov/GritLM_-_GritLM-8x7B-KTO-gguf:Q4_K_M

Run and chat with the model

lemonade run user.GritLM_-_GritLM-8x7B-KTO-gguf-Q4_K_M

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Quantization made by Richard Erkhov.

Github

Discord

Request more models

GritLM-8x7B-KTO - GGUF

Model creator: https://huggingface.co/GritLM/
Original model: https://huggingface.co/GritLM/GritLM-8x7B-KTO/

Name	Quant method	Size
GritLM-8x7B-KTO.Q2_K.gguf	Q2_K	16.12GB
GritLM-8x7B-KTO.IQ3_XS.gguf	IQ3_XS	18.02GB
GritLM-8x7B-KTO.IQ3_S.gguf	IQ3_S	19.03GB
GritLM-8x7B-KTO.Q3_K_S.gguf	Q3_K_S	19.03GB
GritLM-8x7B-KTO.IQ3_M.gguf	IQ3_M	19.96GB
GritLM-8x7B-KTO.Q3_K.gguf	Q3_K	21.0GB
GritLM-8x7B-KTO.Q3_K_M.gguf	Q3_K_M	21.0GB
GritLM-8x7B-KTO.Q3_K_L.gguf	Q3_K_L	22.51GB
GritLM-8x7B-KTO.IQ4_XS.gguf	IQ4_XS	23.63GB
GritLM-8x7B-KTO.Q4_0.gguf	Q4_0	24.63GB
GritLM-8x7B-KTO.IQ4_NL.gguf	IQ4_NL	24.91GB
GritLM-8x7B-KTO.Q4_K_S.gguf	Q4_K_S	24.91GB
GritLM-8x7B-KTO.Q4_K.gguf	Q4_K	26.49GB
GritLM-8x7B-KTO.Q4_K_M.gguf	Q4_K_M	26.49GB
GritLM-8x7B-KTO.Q4_1.gguf	Q4_1	27.32GB
GritLM-8x7B-KTO.Q5_0.gguf	Q5_0	30.02GB
GritLM-8x7B-KTO.Q5_K_S.gguf	Q5_K_S	30.02GB
GritLM-8x7B-KTO.Q5_K.gguf	Q5_K	30.95GB
GritLM-8x7B-KTO.Q5_K_M.gguf	Q5_K_M	30.95GB
GritLM-8x7B-KTO.Q5_1.gguf	Q5_1	32.71GB
GritLM-8x7B-KTO.Q6_K.gguf	Q6_K	35.74GB
GritLM-8x7B-KTO.Q8_0.gguf	Q8_0	46.22GB

Original model description:

pipeline_tag: text-generation inference: true license: apache-2.0 datasets:

GritLM/tulu2

Model Summary

A KTO version of https://huggingface.co/GritLM/GritLM-8x7B

GritLM is a generative representational instruction tuned language model. It unifies text representation (embedding) and text generation into a single model achieving state-of-the-art performance on both types of tasks.

Repository: ContextualAI/gritlm
Paper: https://arxiv.org/abs/2402.09906
Logs: https://wandb.ai/muennighoff/gritlm/runs/0uui712t/overview
Script: https://github.com/ContextualAI/gritlm/blob/main/scripts/training/train_gritlm_7b.sh

Model	Description
GritLM 7B	Mistral 7B finetuned using GRIT
GritLM 8x7B	Mixtral 8x7B finetuned using GRIT

Use

The model usage is documented here.

Citation

@misc{muennighoff2024generative,
      title={Generative Representational Instruction Tuning}, 
      author={Niklas Muennighoff and Hongjin Su and Liang Wang and Nan Yang and Furu Wei and Tao Yu and Amanpreet Singh and Douwe Kiela},
      year={2024},
      eprint={2402.09906},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Downloads last month: 108

GGUF

Model size

47B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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Papers for RichardErkhov/GritLM_-_GritLM-8x7B-KTO-gguf

Generative Representational Instruction Tuning

Paper • 2402.09906 • Published Feb 15, 2024 • 54

KTO: Model Alignment as Prospect Theoretic Optimization

Paper • 2402.01306 • Published Feb 2, 2024 • 22