Instructions to use RichardErkhov/DopeorNope_-_only_mini-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RichardErkhov/DopeorNope_-_only_mini-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="RichardErkhov/DopeorNope_-_only_mini-gguf",
	filename="only_mini.IQ3_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use RichardErkhov/DopeorNope_-_only_mini-gguf with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf RichardErkhov/DopeorNope_-_only_mini-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RichardErkhov/DopeorNope_-_only_mini-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf RichardErkhov/DopeorNope_-_only_mini-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RichardErkhov/DopeorNope_-_only_mini-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/DopeorNope_-_only_mini-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RichardErkhov/DopeorNope_-_only_mini-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/DopeorNope_-_only_mini-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RichardErkhov/DopeorNope_-_only_mini-gguf:Q4_K_M

Use Docker

docker model run hf.co/RichardErkhov/DopeorNope_-_only_mini-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use RichardErkhov/DopeorNope_-_only_mini-gguf with Ollama:
```
ollama run hf.co/RichardErkhov/DopeorNope_-_only_mini-gguf:Q4_K_M
```

Unsloth Studio

How to use RichardErkhov/DopeorNope_-_only_mini-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/DopeorNope_-_only_mini-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/DopeorNope_-_only_mini-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/DopeorNope_-_only_mini-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use RichardErkhov/DopeorNope_-_only_mini-gguf with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/DopeorNope_-_only_mini-gguf:Q4_K_M
```

Lemonade

How to use RichardErkhov/DopeorNope_-_only_mini-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RichardErkhov/DopeorNope_-_only_mini-gguf:Q4_K_M

Run and chat with the model

lemonade run user.DopeorNope_-_only_mini-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

only_mini - GGUF

Model creator: https://huggingface.co/DopeorNope/
Original model: https://huggingface.co/DopeorNope/only_mini/

Name	Quant method	Size
only_mini.Q2_K.gguf	Q2_K	0.54GB
only_mini.IQ3_XS.gguf	IQ3_XS	0.58GB
only_mini.IQ3_S.gguf	IQ3_S	0.6GB
only_mini.Q3_K_S.gguf	Q3_K_S	0.6GB
only_mini.IQ3_M.gguf	IQ3_M	0.61GB
only_mini.Q3_K.gguf	Q3_K	0.64GB
only_mini.Q3_K_M.gguf	Q3_K_M	0.64GB
only_mini.Q3_K_L.gguf	Q3_K_L	0.68GB
only_mini.IQ4_XS.gguf	IQ4_XS	0.7GB
only_mini.Q4_0.gguf	Q4_0	0.72GB
only_mini.IQ4_NL.gguf	IQ4_NL	0.72GB
only_mini.Q4_K_S.gguf	Q4_K_S	0.72GB
only_mini.Q4_K.gguf	Q4_K	0.75GB
only_mini.Q4_K_M.gguf	Q4_K_M	0.75GB
only_mini.Q4_1.gguf	Q4_1	0.77GB
only_mini.Q5_0.gguf	Q5_0	0.83GB
only_mini.Q5_K_S.gguf	Q5_K_S	0.83GB
only_mini.Q5_K.gguf	Q5_K	0.85GB
only_mini.Q5_K_M.gguf	Q5_K_M	0.85GB
only_mini.Q5_1.gguf	Q5_1	0.89GB
only_mini.Q6_K.gguf	Q6_K	0.95GB
only_mini.Q8_0.gguf	Q8_0	1.23GB

Original model description:

library_name: transformers tags: []

Model Card for Model ID

Model Details

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

Developed by: [More Information Needed]
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Model type: [More Information Needed]
Language(s) (NLP): [More Information Needed]
License: [More Information Needed]
Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

Repository: [More Information Needed]
Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]

Uses

Direct Use

[More Information Needed]

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: [More Information Needed]
Hours used: [More Information Needed]
Cloud Provider: [More Information Needed]
Compute Region: [More Information Needed]
Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]

Downloads last month: 51

GGUF

Model size

1B params

Architecture

llama

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

Paper for RichardErkhov/DopeorNope_-_only_mini-gguf

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 53