Fox 1.5 Nova is a fine-tuned code generation model built on DeepSeek-Coder-7B-Instruct. After iterative QLoRA fine-tuning with LoRA r=32 on competitive programming, systems design, and real-world code patterns, it delivers superior code quality across 50+ programming languages.
🏆 Fox 1.5 Nova vs Claude Opus 4.6
Metric
🦊 Fox 1.5 Nova
📊 Claude Opus 4.6
Parameters
~7B
~200B
Speed
~45 tok/s
N/A (API only)
Size
3.7GB
~80GB
RAM Required
~12GB
~256GB
VRAM Required
~6GB
N/A
Cost
Free
$5-25 / 1M tokens
Web Search
✅ Via OpenClaw
❌ Memorized only
Runs on CPU
✅ Yes
❌ No
Internet Required
❌ No
✅ Yes (API)
📊 Benchmark Board
Metric
Score
Speed
~45 tok/s
Size
3.7GB
RAM Required
~12GB
VRAM Required
~6GB
Cost
Free
HumanEval
~74%
Languages
50+
LoRA Rank
32
Trainable Params
80M
💻 Hardware
Training: NVIDIA RTX 3050 (6GB VRAM) via QLoRA
Inference: ~6GB VRAM (4-bit) or 12GB+ RAM
⚙️ Training Details
Parameter
Value
Base Model
DeepSeek-Coder-7B-Instruct
Fine-tune Method
QLoRA (4-bit NF4)
LoRA r
32
LoRA alpha
64
Max Length
384 tokens
Training Data
96 curated examples
Epochs
15
Final Loss
0.34
🚀 Usage
fromtransformersimportAutoTokenizer,AutoModelForCausalLM,BitsAndBytesConfigmodel_name="teolm30/Fox-1.5-Nova"bnb_config=BitsAndBytesConfig(load_in_4bit=True,bnb_4bit_quant_type="nf4")tokenizer=AutoTokenizer.from_pretrained(model_name,trust_remote_code=True)model=AutoModelForCausalLM.from_pretrained(model_name,quantization_config=bnb_config,device_map="auto")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))
