Instructions to use klusai/tf2-12b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use klusai/tf2-12b-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="klusai/tf2-12b-gguf",
	filename="f16/tf2-12b-8k-fp16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use klusai/tf2-12b-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/tf2-12b-gguf:F16
# Run inference directly in the terminal:
llama-cli -hf klusai/tf2-12b-gguf:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf klusai/tf2-12b-gguf:F16
# Run inference directly in the terminal:
llama-cli -hf klusai/tf2-12b-gguf: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 klusai/tf2-12b-gguf:F16
# Run inference directly in the terminal:
./llama-cli -hf klusai/tf2-12b-gguf: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 klusai/tf2-12b-gguf:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf klusai/tf2-12b-gguf:F16

Use Docker

docker model run hf.co/klusai/tf2-12b-gguf:F16

LM Studio
Jan

vLLM

How to use klusai/tf2-12b-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "klusai/tf2-12b-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "klusai/tf2-12b-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/klusai/tf2-12b-gguf:F16

Ollama
How to use klusai/tf2-12b-gguf with Ollama:
```
ollama run hf.co/klusai/tf2-12b-gguf:F16
```

Unsloth Studio

How to use klusai/tf2-12b-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/tf2-12b-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/tf2-12b-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/tf2-12b-gguf to start chatting

Docker Model Runner
How to use klusai/tf2-12b-gguf with Docker Model Runner:
```
docker model run hf.co/klusai/tf2-12b-gguf:F16
```

Lemonade

How to use klusai/tf2-12b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull klusai/tf2-12b-gguf:F16

Run and chat with the model

lemonade run user.tf2-12b-gguf-F16

List all available models

lemonade list

🦊 klusai/tf2-12b — LoRA-tuned EN→RO Translator (Fables) — GGUF build

klusai/tf2-12b is a Gemma 3 12B (Instruct) based translator, fine-tuned with LoRA on 15,000 English→Romanian fable pairs (synthetic, generated with GPT-o3). This repository distributes the merged weights in GGUF format for fast inference with llama.cpp-compatible runtimes.

Focus: faithful, fluent EN→RO translation of short moral fables, preserving tone, structure, and morals (with proper Romanian diacritics).

✨ Highlights

Task: EN→RO translation for narrative prose (fables).
Training: Parameter-efficient LoRA; adapters merged into base and exported to GGUF.
Data: 15k synthetic pairs generated by GPT-o3.
Format: GGUF (llama.cpp). Multiple quantizations may be provided (e.g. Q5_K_M, F16).

🧱 I/O Contract

Input: English fable paragraph(s) + concise instruction to translate to Romanian.
Output: Romanian translation that is faithful, fluent, and keeps the moral explicit.

Prompting tip: “Translate to Romanian with proper diacritics. Preserve meaning, tone, and moral. Avoid adding content.”

🧪 Training Summary

Base model: Gemma 3 12B (Instruct)
Method: LoRA fine-tuning; adapters merged for deployment
Dataset: 15k EN→RO fable translations (GPT-o3 generated)

✅ Intended Use

Education/NLP demos, EN→RO machine translation of narrative content, style-preserving localization.

⚠️ Limitations

Tuned on fables; may underperform on legal/medical/colloquial domains.
Synthetic data can carry stylistic biases or occasional literalism.
Always add human review for production use.

🔑 Licensing

This GGUF build: MIT
Use must also comply with the Gemma base model license/terms.

📝 Changelog

v1.0 (GGUF) — Initial public GGUF release (LoRA-merged). Multiple quantizations provided (e.g., Q5_K_M).

Downloads last month: 8

GGUF

Model size

12B params

Architecture

gemma3

Hardware compatibility

5-bit

16-bit

klusai
/

tf2-12b-gguf