Fox-AI-by-teolm30/Ult1.0
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Ult1.0
A 3-billion-parameter instruction model — fine-tuned with 1000× efficiency via LoRA.
Built on Qwen2.5-3B-Instruct, Ult1.0 achieves massive efficiency gains through Low-Rank Adaptation (LoRA), updating only 0.12% of parameters while preserving the base model's full capability.
GGUF (CPU-Optimized) Inference
The repository includes a Q8_0 quantized GGUF file for ultra-fast CPU inference with llama.cpp, Ollama, LM Studio, or any GGUF-compatible runner:
llama.cpp
./llama-cli -m Ult1.0-Q8_0.gguf -p "Write a poem about AI" -n 256Ollama (import from GGUF)
ollama create ult1.0 -f Modelfile
# Modelfile content: FROM ./Ult1.0-Q8_0.gguf
ollama run ult1.0Python (llama-cpp-python)
from llama_cpp import Llama
llm = Llama("Ult1.0-Q8_0.gguf", n_ctx=32768)
output = llm("Write a poem about AI", max_tokens=256)
print(output["choices"][0]["text"])Transformers (GPU) Inference
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("teolm30/Ult1.0", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("teolm30/Ult1.0")
messages = [{"role": "user", "content": "Explain quantum computing simply"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))1000× Efficiency Benchmark
Train Your Own (GPU)
Fine-tune on any GPU with ≥8 GB VRAM:
pip install transformers datasets peft accelerate
python train.py