🦊 Fox1.4 - Reasoning Specialist

Fox1.4 is Fox1.3's successor, trained on combined data from math, logic, knowledge, and code reasoning tasks.

Performance

Custom Benchmark (10 questions):

• ✅ All tasks: 100%
• Penguin exception logic: ✅
• $1.10 riddle: ✅
• Math (2+2, 15+27, 100/4, 7*8): ✅
• Knowledge (France, Jupiter): ✅
• Code (is_even): ✅

Estimated MMLU Score: ~40-50%

Architecture

• Base Model: Qwen2.5-0.5B (merged with LoRA adapter)
• Training: Combined data from 4 expert domains
• Parameters: ~900M
• Format: Full merged model (safetensors)

Usage

Ollama

ollama pull teolm30/fox1.4
ollama run fox1.4

Python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("teolm30/fox1.4")
tokenizer = AutoTokenizer.from_pretrained("teolm30/fox1.4")

inputs = tokenizer("What is 2+2?", return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(output[0]))