1.5 KiB
1.5 KiB
title, emoji, colorFrom, colorTo, sdk, app_port, pinned, license, tags, model_type, widget, inference
| title | emoji | colorFrom | colorTo | sdk | app_port | pinned | license | tags | model_type | widget | inference | ||||||||||||||||||
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| Fox1.4 | 🦊 | blue | purple | static | 7860 | false | apache-2.0 |
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qwen2 |
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🦊 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]))
🤖 Run with Ollama
ollama run hf.co/teolm30/fox1.4