Instructions to use marantral/first-test-weeb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use marantral/first-test-weeb with Transformers:

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("marantral/first-test-weeb")
model = AutoModelForSeq2SeqLM.from_pretrained("marantral/first-test-weeb")

llama-cpp-python

How to use marantral/first-test-weeb with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="marantral/first-test-weeb",
	filename="first-test-weeb.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 marantral/first-test-weeb with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf marantral/first-test-weeb
# Run inference directly in the terminal:
llama-cli -hf marantral/first-test-weeb

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf marantral/first-test-weeb
# Run inference directly in the terminal:
llama-cli -hf marantral/first-test-weeb

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 marantral/first-test-weeb
# Run inference directly in the terminal:
./llama-cli -hf marantral/first-test-weeb

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 marantral/first-test-weeb
# Run inference directly in the terminal:
./build/bin/llama-cli -hf marantral/first-test-weeb

Use Docker

docker model run hf.co/marantral/first-test-weeb

LM Studio
Jan
Ollama
How to use marantral/first-test-weeb with Ollama:
```
ollama run hf.co/marantral/first-test-weeb
```

Unsloth Studio

How to use marantral/first-test-weeb 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 marantral/first-test-weeb 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 marantral/first-test-weeb to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for marantral/first-test-weeb to start chatting

Docker Model Runner
How to use marantral/first-test-weeb with Docker Model Runner:
```
docker model run hf.co/marantral/first-test-weeb
```

Lemonade

How to use marantral/first-test-weeb with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull marantral/first-test-weeb

Run and chat with the model

lemonade run user.first-test-weeb-{{QUANT_TAG}}

List all available models

lemonade list

first-test-weeb

This model is a fine-tuned version of google-t5/t5-small on an unknown dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3.0

Training results

Framework versions

Transformers 4.45.0.dev0
Pytorch 2.4.1+cu121
Datasets 3.0.0
Tokenizers 0.19.1

Downloads last month: 19

Safetensors

Model size

60.5M params

Tensor type

F32

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for marantral/first-test-weeb

Base model

google-t5/t5-small

Quantized

(20)

this model