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

Libraries

How to use promptagainstthemachine/Thinkmini with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "promptagainstthemachine/Thinkmini")

Transformers

How to use promptagainstthemachine/Thinkmini with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("promptagainstthemachine/Thinkmini")
model = AutoModelForCausalLM.from_pretrained("promptagainstthemachine/Thinkmini")
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 promptagainstthemachine/Thinkmini with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/promptagainstthemachine/Thinkmini

SGLang

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

Unsloth Studio new

How to use promptagainstthemachine/Thinkmini 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 promptagainstthemachine/Thinkmini 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 promptagainstthemachine/Thinkmini to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for promptagainstthemachine/Thinkmini to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="promptagainstthemachine/Thinkmini",
    max_seq_length=2048,
)

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

Model Card for Model ID

Its a very simple model for text generation built on top of Llama3.2-1B.
It is very lightweight and can be inferenced on a CPU with 4 gb RAM.
Developed by: findthehead

Framework versions

PEFT 0.17.1

Inference Code

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

model_name = "Prachir-AI/Thinkmini"
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Create a BitsAndBytesConfig to enable 4-bit loading
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True, # Enable 4-bit loading as intended for this model
    bnb_4bit_quant_type="nf4", # This is a common default for 4-bit models
    bnb_4bit_compute_dtype=torch.bfloat16, # Use bfloat16 for computation
    bnb_4bit_use_double_quant=True, # Often used with nf4
)

# Load the model with the configured 4-bit quantization
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    quantization_config=bnb_config,
    torch_dtype=torch.bfloat16 # Ensure the model itself is loaded with bfloat16 dtypes where applicable
)

inputs = tokenizer("How do you plan for a full pentest of a web application?", return_tensors="pt").to('cuda')
  # inference mode

output_ids = model.generate(
    **inputs,
    max_new_tokens=500,
    temperature=0.7,
    top_p=0.9
)

print(tokenizer.decode(output_ids[0], skip_special_tokens=True))

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Safetensors

Model size

1B params

Tensor type

F32

F16

promptagainstthemachine
/

Thinkmini

Model Card for Model ID

Framework versions

Inference Code

Dataset used to train promptagainstthemachine/Thinkmini