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

Libraries

How to use nativemind/mozgach_final_model with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nativemind/mozgach_final_model")
model = AutoModelForCausalLM.from_pretrained("nativemind/mozgach_final_model", 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 nativemind/mozgach_final_model with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/nativemind/mozgach_final_model

SGLang

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

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

nativemind commited on Aug 2, 2025

Commit

aeaa98b

1 Parent(s): 5378d92

Cleaning

Browse files

Files changed (8) hide show

README.md +0 -4
config.json +0 -28
merges.txt +0 -0
scheduler.pt +0 -3
special_tokens_map.json +0 -1
tokenizer_config.json +0 -1
training_args.bin +0 -3
vocab.json +0 -0

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-# TrueGPT Small: AI Model for Action and Empowerment
-TrueGPT Small is a lightweight version of the TrueGPT artificial intelligence model, designed for users who need the empowering and actionable features of TrueGPT with reduced computational requirements. By providing actionable solutions and eliminating uncertainty, TrueGPT Small retains the core features of the original TrueGPT while making it accessible to a wider range of devices and systems. With seamless integration to the Hugging Face ecosystem, users can easily utilize TrueGPT Small for various AI applications.

config.json DELETED Viewed

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