Instructions to use tarvico/vytre_core with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tarvico/vytre_core with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tarvico/vytre_core")
model = AutoModelForCausalLM.from_pretrained("tarvico/vytre_core", device_map="auto")
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 Settings

vLLM

How to use tarvico/vytre_core with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/tarvico/vytre_core

SGLang

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

Docker Model Runner
How to use tarvico/vytre_core with Docker Model Runner:
```
docker model run hf.co/tarvico/vytre_core
```

Vytre Core

Vytre Core is a compact, domain-specific text-generation model trained on synthetic enterprise-workforce tasks: department creation, agent definition, workflow planning, task decomposition, governance checks, and tool routing.

Model files

This repository is self-contained. It contains a standard Transformers GPT-2 checkpoint and tokenizer, not a LoRA adapter. Do not combine it with the legacy vytre-core-upload LoRA template or an external Llama base model.

Load with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "tarvico/vytre-core"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

prompt = (
    "You are Vytre, an enterprise workforce operating intelligence model.\\n"
    "Input: Create marketing department\\n"
    "Output: "
)
inputs = tokenizer(prompt, return_tensors="pt")
tokens = model.generate(
    **inputs,
    max_new_tokens=80,
    do_sample=False,
    pad_token_id=tokenizer.pad_token_id,
    eos_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))

Limitations

This is a small specialised model, not a general-purpose chat model. Use the prompt format above and keep requests close to the listed operational domains. Validate generated JSON before taking actions from it.

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Safetensors

Model size

1.7M params

Tensor type

F32