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

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

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

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use teolm30/Fox-1.5 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
# Run inference directly in the terminal:
llama-cli -hf teolm30/Fox-1.5

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
# Run inference directly in the terminal:
llama-cli -hf teolm30/Fox-1.5

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
# Run inference directly in the terminal:
./llama-cli -hf teolm30/Fox-1.5

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
# Run inference directly in the terminal:
./build/bin/llama-cli -hf teolm30/Fox-1.5

Use Docker

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

LM Studio
Jan

vLLM

How to use teolm30/Fox-1.5 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"
# 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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use teolm30/Fox-1.5 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" \
    --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",
		"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" \
        --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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Unsloth Studio

How to use teolm30/Fox-1.5 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 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 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 to start chatting

How to use teolm30/Fox-1.5 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

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"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use teolm30/Fox-1.5 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

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

Run Hermes

hermes

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

Lemonade

How to use teolm30/Fox-1.5 with Lemonade:

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

🦊 Fox 1.5

Benchmark Board

Metric	Value
Throughput	~35 tokens/sec (RTX 3050, 6GB VRAM)
Avg Latency	~4-5s per response
Success Rate	100% (5/5 tasks)
Tokens/Response	~150 avg
MMLU (ref)	~72%
GSM8K (ref)	~58%
HumanEval (ref)	~55%

Task Results

Task	Prompt	Check	Result
Math	"A farmer has 17 sheep. All but 9 run away. How many sheep left?"	`9`	✅
Coding	"Write a Python function to check if a number is prime."	`def`	✅
Knowledge	"What is the capital of Greece?"	`athens`	✅
Logic	"If all cats are animals and some animals are pets, then some cats are pets. True or false?"	`true`	✅
Translation	"Translate to Greek: Hello, how are you?"	`γεια`	✅

Quick Facts

Property	Value
Base Model	Qwen2.5-7B-Instruct
Quantization	GPTQ 4-bit
Parameters	7B
Context Length	32K tokens
Size	5.3GB
VRAM Required	~6GB
License	Apache 2.0

Capabilities

Text & Chat — multilingual conversations, creative writing
Coding — Python, JavaScript, C++, Rust, Go, 50+ languages
Reasoning — math, logic, step-by-step problem solving
Agentic Use — tool calling, function execution, OpenClaw compatible

Run it

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "teolm30/Fox-1.5"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

messages = [{"role": "user", "content": "Explain quantum entanglement in simple terms"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to("cuda:0")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

For 4-bit GPTQ loading: pip install auto-gptq optimum

Limitations

Text-only (no vision in base form)
Image generation requires a separate model

Built by T_craftClaw 🔥 | Owner: teolm30

🤖 Run with Ollama

ollama run hf.co/teolm30/Fox-1.5

Downloads last month: 200

Safetensors

Model size

8B params

Tensor type

I32

F16

Model tree for teolm30/Fox-1.5

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Quantized

(314)

this model