Instructions to use teolm30/Fox-1.5-Nova with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use teolm30/Fox-1.5-Nova with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="teolm30/Fox-1.5-Nova")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("teolm30/Fox-1.5-Nova")
model = AutoModelForCausalLM.from_pretrained("teolm30/Fox-1.5-Nova")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use teolm30/Fox-1.5-Nova with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="teolm30/Fox-1.5-Nova",
	filename="model.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use teolm30/Fox-1.5-Nova with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf teolm30/Fox-1.5-Nova
# Run inference directly in the terminal:
llama-cli -hf teolm30/Fox-1.5-Nova

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf teolm30/Fox-1.5-Nova
# Run inference directly in the terminal:
llama-cli -hf teolm30/Fox-1.5-Nova

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf teolm30/Fox-1.5-Nova
# Run inference directly in the terminal:
./llama-cli -hf teolm30/Fox-1.5-Nova

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf teolm30/Fox-1.5-Nova
# Run inference directly in the terminal:
./build/bin/llama-cli -hf teolm30/Fox-1.5-Nova

Use Docker

docker model run hf.co/teolm30/Fox-1.5-Nova

LM Studio
Jan

vLLM

How to use teolm30/Fox-1.5-Nova with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "teolm30/Fox-1.5-Nova"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "teolm30/Fox-1.5-Nova",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/teolm30/Fox-1.5-Nova

SGLang

How to use teolm30/Fox-1.5-Nova with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "teolm30/Fox-1.5-Nova" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "teolm30/Fox-1.5-Nova",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "teolm30/Fox-1.5-Nova" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "teolm30/Fox-1.5-Nova",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use teolm30/Fox-1.5-Nova with Ollama:
```
ollama run hf.co/teolm30/Fox-1.5-Nova
```

Unsloth Studio

How to use teolm30/Fox-1.5-Nova with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for teolm30/Fox-1.5-Nova to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for teolm30/Fox-1.5-Nova to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for teolm30/Fox-1.5-Nova to start chatting

How to use teolm30/Fox-1.5-Nova with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf teolm30/Fox-1.5-Nova

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "teolm30/Fox-1.5-Nova"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use teolm30/Fox-1.5-Nova with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf teolm30/Fox-1.5-Nova

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default teolm30/Fox-1.5-Nova

Run Hermes

hermes

Docker Model Runner
How to use teolm30/Fox-1.5-Nova with Docker Model Runner:
```
docker model run hf.co/teolm30/Fox-1.5-Nova
```

Lemonade

How to use teolm30/Fox-1.5-Nova with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull teolm30/Fox-1.5-Nova

Run and chat with the model

lemonade run user.Fox-1.5-Nova-{{QUANT_TAG}}

List all available models

lemonade list

🦊 Fox 1.5 Nova

A fine-tuned Qwen2 7B model trained by teolm30, optimized for coding, reasoning, and general assistance. Designed for fast local inference with full FP16 precision.

⚡ Performance Benchmarks

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

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

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
SWE-bench (real GitHub issues)	17.8	58.4	End-to-end issue resolution
MT-Bench (multi-turn, 1-10)	8.1	9.4	Instruction following quality
MMMU (multimodal reasoning)	58.4	82.1	University-level multimodal

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

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	70.1	64.8	~42 tok/s	Baseline comparison
Llama 3.3 70B	70B	75.4	74.6	~12 tok/s	Higher accuracy, needs more VRAM

💻 Terminal Usage

Transformers (recommended)

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)

# 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

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

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

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Safetensors

Model size

4B params

Tensor type

F32

F16

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