TX-8G

TX-8G

Local AI model optimized for consumer hardware. Runs on 8GB RAM.

TX-8G is TARX's default model, designed to run efficiently on most modern computers while delivering strong performance across general tasks.

Model Details

Capabilities

• ✅ General conversation
• ✅ Writing assistance
• ✅ Code explanation & simple generation
• ✅ Document analysis
• ✅ Image understanding (vision)
• ✅ Research & summarization

Performance

Benchmarks vs comparable models:

Full benchmarks coming Q1 2026

Usage

With TARX Desktop (Recommended)

Download TARX and the model is included:

https://tarx.com/download

With Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Tarxxxxxx/TX-8G"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype="auto"
)

messages = [
    {"role": "user", "content": "Explain how local AI protects privacy."}
]

input_ids = tokenizer.apply_chat_template(
    messages,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(
    input_ids,
    max_new_tokens=512,
    do_sample=True,
    temperature=0.7
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

With llama.cpp

# Download GGUF
wget https://huggingface.co/Tarxxxxxx/TX-8G/resolve/main/tx-8g.Q8_0.gguf

# Run with llama.cpp
./main -m tx-8g.Q8_0.gguf -p "Hello, I'm TARX." -n 256

With Ollama

ollama run tarx/tx-8g

Hardware Requirements

Quantization Options

Training

TX-8G is fine-tuned from Qwen2.5-7B-Instruct with:

• Additional instruction tuning for local-first use cases
• Optimization for consumer hardware inference
• Enhanced privacy-aware responses

Training data does not include any TARX user conversations (we don't have access to them).

Ethical Considerations

TX-8G is designed for local, private use. Because it runs on user devices:

• No user data is collected
• No conversations are logged
• No usage is monitored
• Users have complete control

Citation

@misc{tarx2026tx8g,
  title={TX-8G: Local-First Language Model for Consumer Hardware},
  author={TARX Team},
  year={2026},
  publisher={HuggingFace},
  url={https://huggingface.co/Tarxxxxxx/TX-8G}
}

Links

• TARX Website
• Documentation
• Download Desktop App
• Model Benchmarks

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