Fox 1.5 Nova is Teo's code generation model, fine-tuned on DeepSeek-Coder-7B-Instruct for competitive programming, systems design, and real-world code patterns across 50+ languages.
🏆 Comparison
Metric
🦊 Fox 1.5 Nova (7B)
Claude Mythos
Parameters
~7B
~200B+
Speed
~40+ tok/s (fp16)
N/A (API only)
Size
6.6GB (4-bit) / 13GB (fp16)
~80GB
RAM Required
~16GB
~256GB
VRAM Required
~6GB
N/A
Cost
Free
$5-25 / 1M tokens
Runs on CPU
✅ Yes
❌ No
Internet Required
❌ No
✅ Yes
📊 Specs
Metric
Value
Base Model
DeepSeek-Coder-7B-Instruct
Fine-tune Method
QLoRA (4-bit NF4)
LoRA r
16
LoRA alpha
64
Max Length
512 tokens
Trainable Params
~40M
Training Steps
220
Epochs
10
Output Precision
fp16 merged
💻 Hardware
Training: NVIDIA RTX 3050 (6GB VRAM) via QLoRA + Unsloth
Inference: ~6GB VRAM (4-bit) or fp16 with 8GB+ VRAM
🚀 Usage
fromtransformersimportAutoTokenizer,AutoModelForCausalLMmodel_name="teolm30/Fox-1.5-Nova"tokenizer=AutoTokenizer.from_pretrained(model_name,trust_remote_code=True)model=AutoModelForCausalLM.from_pretrained(model_name,device_map="auto",torch_dtype=torch.float16)prompt="Write a Python LRU cache"messages=[{"role":"user","content":prompt}]text=tokenizer.apply_chat_template(messages,tokenize=False,add_generation_prompt=True)inputs=tokenizer(text,return_tensors="pt").to("cuda")outputs=model.generate(**inputs,max_new_tokens=512,do_sample=True,temperature=0.7)print(tokenizer.decode(outputs[0],skip_special_tokens=True))
⚠️ Limitations
fp16 model is 13GB — requires more VRAM than 4-bit version
For 4-bit version (~6.6GB), see teolm30/Fox-1.5-Nova-4bit
No built-in tool-use (use OpenClaw agent framework)
