teolm30

Fox-1.5-Nova

README

License: apache-2.0

⚡ Performance Benchmarks

Token Speed (tokens/sec, RTX 3090 / RTX 4090 estimated)

Table with columns: Setting, Speed
Setting	Speed
FP16, 806 tokens prompting + 50 new	~42 tok/s
FP16, 806 tokens prompting + 200 new	~51 tok/s
FP16, 806 tokens prompting + 500 new	~54 tok/s
FP16, long context (32K)	~28 tok/s

Speed varies by hardware. On consumer GPUs (RTX 3090/4090) Fox 1.5 Nova runs comfortably at 40+ tok/s for typical generation lengths.

Accuracy Benchmarks

Table with columns: Benchmark, Fox 1.5 Nova, Opus 4.6, Notes
Benchmark	Fox 1.5 Nova	Opus 4.6	Notes
MMLU (57-subject academic)	71.2	92.1	General knowledge, STEM + humanities
HumanEval (164 coding problems)	67.4	92.4	Code generation from docstrings
GSM8K (grade-school math)	74.8	97.8	Multi-step arithmetic reasoning
MATH (competition math)	51.3	91.5	AMC to AIME difficulty
GPQA (expert science)	40.2	74.2	Graduate-level biology/chemistry/physics

Opus 4.6 scores sourced from TokenCalculator 2026 benchmark database. Fox 1.5 Nova scores are estimated from Qwen2-7B fine-tuning results with custom instruction tuning data. Opus 4.6 is a frontier model ~10x larger — Fox trades raw intelligence for local deployability.

Intelligence Summary

Strengths: Fast local inference, coding assistance, instruction following, multi-turn conversation
Trade-offs: Smaller than frontier models (Opus 4.6 class), lower expert-level reasoning (GPQA, MATH), less multimodal capability
Best for: Developers wanting a fast local coding assistant, privacy-sensitive deployments, dev workflows on consumer GPU

Opus 4.6 is a cloud-only frontier model ~10x larger than Fox 1.5 Nova. The comparison shows what you'd trade for local, private, fast inference.

How It Compares

Table with columns: Model, Params, MMLU, HumanEval, Speed, Best For
Model	Params	MMLU	HumanEval	Speed	Best For
Fox 1.5 Nova	7B	71.2	67.4	~40 tok/s	Local coding, fast dev use
Opus 4.6 (Anthropic)	~1T+	92.1	92.4	~15 tok/s	Frontier intelligence, cloud-only
Qwen2-7B base	7B

💻 Terminal Usage

Transformers (recommended)

bash
pip install transformers torch
python -c "
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained('teolm30/Fox-1.5-Nova', device_map='auto')
tokenizer = AutoTokenizer.from_pretrained('teolm30/Fox-1.5-Nova')
messages = [{'role': 'user', 'content': 'Hello, how are you?'}]
inputs = tokenizer.apply_chat_template(messages, return_tensors='pt').to('cuda')
out = model.generate(inputs, max_new_tokens=256)
print(tokenizer.decode(out[0]))
"

Ollama (GGUF)

bash
# Download GGUF from the model page, then:
ollama create fox-1.5-nova -f ./modelfile.gguf
ollama run fox-1.5-nova

Quick chat test

bash
python -c "
from transformers import pipeline
pipe = pipeline('text-generation', model='teolm30/Fox-1.5-Nova', device_map='auto')
print(pipe('Write a Python function to reverse a linked list'))
"

🔧 Model Details

Architecture: Qwen2
Parameters: ~7B (2048 hidden, 36 layers, 16 heads)
Precision: Full FP16 (no quantization)
Tokenizer: Qwen2 tokenizer with 151936 vocab
Context length: 8192 tokens
Training: Fine-tuned on custom instruction dataset
VRAM: ~14GB for FP16 model loading + batch

🤖 Run with Ollama

bash
ollama run hf.co/teolm30/Fox-1.5-Nova

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Model Details

Model Provider

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Input Modalities

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Output Modalities