Instructions to use klusai/tf2-1b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use klusai/tf2-1b-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="klusai/tf2-1b-gguf",
	filename="tf2-1b-fp32.gguf",
)

llm.create_chat_completion(
	messages = "\"Меня зовут Вольфганг и я живу в Берлине\""
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use klusai/tf2-1b-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf klusai/tf2-1b-gguf
# Run inference directly in the terminal:
llama-cli -hf klusai/tf2-1b-gguf

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf klusai/tf2-1b-gguf
# Run inference directly in the terminal:
llama-cli -hf klusai/tf2-1b-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 klusai/tf2-1b-gguf
# Run inference directly in the terminal:
./llama-cli -hf klusai/tf2-1b-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 klusai/tf2-1b-gguf
# Run inference directly in the terminal:
./build/bin/llama-cli -hf klusai/tf2-1b-gguf

Use Docker

docker model run hf.co/klusai/tf2-1b-gguf

LM Studio
Jan
Ollama
How to use klusai/tf2-1b-gguf with Ollama:
```
ollama run hf.co/klusai/tf2-1b-gguf
```

Unsloth Studio new

How to use klusai/tf2-1b-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 klusai/tf2-1b-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 klusai/tf2-1b-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for klusai/tf2-1b-gguf to start chatting

Docker Model Runner
How to use klusai/tf2-1b-gguf with Docker Model Runner:
```
docker model run hf.co/klusai/tf2-1b-gguf
```

Lemonade

How to use klusai/tf2-1b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull klusai/tf2-1b-gguf

Run and chat with the model

lemonade run user.tf2-1b-gguf-{{QUANT_TAG}}

List all available models

lemonade list

🌱 TinyFabulist-TF2-1B · Gemma 3 1B EN→RO Fable Translator

tf2-1b is a parameter-efficiently fine-tuned checkpoint (LoRA adapters merged) of Google Gemma 3 1B that specialises in translating moral fables from English into Romanian.
It was produced during the TinyFabulist-TF2 project and is intended as a lightweight, cost-efficient alternative to GPT-class APIs for literary translation in low-resource settings.

📰 Model Summary


Base model	`google/gemma-3b-1b`
Architecture	Decoder-only Transformer, 1 B params
Fine-tuning method	Supervised SFT (full-sequence), then instruction-tuning, then LoRA adapters (rank = 16) Adapters merged for this release
Training data	12 000 EN–RO fable pairs (train split, TinyFabulist-TF2)
Validation	1 500 pairs
Eval set	1 500 pairs (held-out)
Objective	Next-token cross-entropy on Romanian targets
Hardware / budget	1× A6000 GPU (48 GB) · ~4 h · ≈ $32
Intended use	Off-line literary translation of short stories / fables
Out-of-scope	News, legal, medical, or very long documents; languages other than EN ↔ RO

✨ How It Works

This model translates short English fables or moral stories into fluent, natural Romanian, capturing not just the literal meaning but also the narrative style and ethical lesson. Simply provide a short story in English, and the model will generate a Romanian version that preserves the storytelling tone and clarity, making it suitable for children’s literature, educational content, or creative writing. Designed to be lightweight, it works well even on modest hardware and is intended as a free, accessible alternative to large proprietary translation services. The model is ideal for teachers, students, and researchers looking to generate high-quality literary translations in low-resource or offline settings.

🚧 Limitations & Biases

Trained exclusively on synthetic data → may reproduce GPT-style phrasing. Domain‐specific: excels on short, moralistic narratives; underperforms on technical or colloquial prose. No guard-rails: user must filter harmful content downstream. Context window = 2 048 tokens (≈ 1 500 Romanian words).

✅ License

Released under Apache 2.0. Dataset (TinyFabulist-TF2 EN–RO 15 k) is CC-BY-4.0.

Questions or feedback? Open an issue or DM @klusai. Happy translating! 🚀

Downloads last month: 2

GGUF

Model size

1.0B params

Architecture

gemma3

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Evaluation results

BLEU on TinyFabulist-TF2 (15 k EN–RO fables)
self-reported

21.800
LLM-Eval (5-dim average) on TinyFabulist-TF2 (15 k EN–RO fables)
self-reported

3.75 / 5