Instructions to use Dabococo/Minecraft_Flans_Mod with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Dabococo/Minecraft_Flans_Mod with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Dabococo/Minecraft_Flans_Mod",
	filename="FullPrecision_WW2-Server_Model.F16.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 Dabococo/Minecraft_Flans_Mod with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Dabococo/Minecraft_Flans_Mod:F16
# Run inference directly in the terminal:
llama-cli -hf Dabococo/Minecraft_Flans_Mod:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Dabococo/Minecraft_Flans_Mod:F16
# Run inference directly in the terminal:
llama-cli -hf Dabococo/Minecraft_Flans_Mod:F16

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 Dabococo/Minecraft_Flans_Mod:F16
# Run inference directly in the terminal:
./llama-cli -hf Dabococo/Minecraft_Flans_Mod:F16

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 Dabococo/Minecraft_Flans_Mod:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Dabococo/Minecraft_Flans_Mod:F16

Use Docker

docker model run hf.co/Dabococo/Minecraft_Flans_Mod:F16

LM Studio
Jan
Ollama
How to use Dabococo/Minecraft_Flans_Mod with Ollama:
```
ollama run hf.co/Dabococo/Minecraft_Flans_Mod:F16
```

Unsloth Studio new

How to use Dabococo/Minecraft_Flans_Mod 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 Dabococo/Minecraft_Flans_Mod 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 Dabococo/Minecraft_Flans_Mod to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Dabococo/Minecraft_Flans_Mod to start chatting

Docker Model Runner
How to use Dabococo/Minecraft_Flans_Mod with Docker Model Runner:
```
docker model run hf.co/Dabococo/Minecraft_Flans_Mod:F16
```

Lemonade

How to use Dabococo/Minecraft_Flans_Mod with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Dabococo/Minecraft_Flans_Mod:F16

Run and chat with the model

lemonade run user.Minecraft_Flans_Mod-F16

List all available models

lemonade list

FRENCH / FRANCAIS :

Model Card for Model ID

WARNING The AI model is not specific to Flan’s mod, it still includes NOTHING. BUT a new version of the model is coming soon with all the knowledge of the HBM Nuclear Tech Mod !!

ATTENTION Le modèle d'IA n'est pas dutout spéécialisé en Flan's mod, il comprend encore RIEN. MAIS une nouvelle version du modèle arrive bientôt avec toutes les connaissances du mod HBM Nuclear Tech Mod !!

Modèle crée uniquement pour le serveur Minecraft Openwar Edition WW2 : https://openwar.fr / https://www.technicpack.net/modpack/openwar-ww2-edition.1971713

Model Details

Architecture : Llama3 FineTune sur demande du serveur Openwar.

Model Description

PARTIAL GPU OFFLOAD POSSIBLE

Developed by: Dabococo
Funded by: 0€
Model type: Quantifié Q4
Language(s) (NLP): Français de préférence
License: Apache 2.0
Finetuned from model: Llama3 from Meta

Model Sources [optional]

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

Uses

Utilisable pour des bots discord ou directement inégrer dans le jeu. Adapter afin de guider les joueurs peu habituer au moddé.

Direct Use

Easy in LMStudio

[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]

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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

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

BibTeX:

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APA:

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Glossary [optional]

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Model Card Authors [optional]

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Model Card Contact

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Downloads last month: 20

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

16-bit

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Dataset used to train Dabococo/Minecraft_Flans_Mod

Paper for Dabococo/Minecraft_Flans_Mod

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 45