Instructions to use teolm30/fox1.4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use teolm30/fox1.4 with Transformers:

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

pipe = pipeline("text-generation", model="teolm30/fox1.4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("teolm30/fox1.4")
model = AutoModelForCausalLM.from_pretrained("teolm30/fox1.4")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use teolm30/fox1.4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "teolm30/fox1.4"
# 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/fox1.4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/teolm30/fox1.4

SGLang

How to use teolm30/fox1.4 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/fox1.4" \
    --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/fox1.4",
		"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/fox1.4" \
        --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/fox1.4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use teolm30/fox1.4 with Docker Model Runner:
```
docker model run hf.co/teolm30/fox1.4
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🦊 Fox1.4 - Reasoning Specialist

Fox1.4 is Fox1.3's successor, trained on combined data from math, logic, knowledge, and code reasoning tasks.

Performance

Custom Benchmark (10 questions):

✅ All tasks: 100%
Penguin exception logic: ✅
$1.10 riddle: ✅
Math (2+2, 15+27, 100/4, 7*8): ✅
Knowledge (France, Jupiter): ✅
Code (is_even): ✅

Estimated MMLU Score: ~40-50%

Architecture

Base Model: Qwen2.5-0.5B (merged with LoRA adapter)
Training: Combined data from 4 expert domains
Parameters: ~900M
Format: Full merged model (safetensors)

Usage

Ollama

ollama pull teolm30/fox1.4
ollama run fox1.4

Python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("teolm30/fox1.4")
tokenizer = AutoTokenizer.from_pretrained("teolm30/fox1.4")

inputs = tokenizer("What is 2+2?", return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(output[0]))

Downloads last month: 33

Safetensors

Model size

0.5B params

Tensor type

BF16

Model tree for teolm30/fox1.4

Quantizations

1 model

teolm30
/

fox1.4