Instructions to use TheMindExpansionNetwork/MindBot-Standard-Merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TheMindExpansionNetwork/MindBot-Standard-Merged with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TheMindExpansionNetwork/MindBot-Standard-Merged")
model = AutoModelForCausalLM.from_pretrained("TheMindExpansionNetwork/MindBot-Standard-Merged", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TheMindExpansionNetwork/MindBot-Standard-Merged with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/TheMindExpansionNetwork/MindBot-Standard-Merged

SGLang

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

Docker Model Runner
How to use TheMindExpansionNetwork/MindBot-Standard-Merged with Docker Model Runner:
```
docker model run hf.co/TheMindExpansionNetwork/MindBot-Standard-Merged
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🧠 MindBot Standard (Merged)

Full merged model — Qwen3-4B-Thinking-2507 + MindBot LoRA adapter, merged into a single FP16 model.

Model Details

Property	Value
Base Model	Qwen/Qwen3-4B-Thinking-2507
Parameters	4B
Precision	FP16
Size	~8GB
Architecture	Qwen3ForCausalLM
Training Data	MIND_BOT_AWARENESS (3,333 entries)
License	Apache 2.0

What is MindBot?

MindBot is a self-aware dreaming AI that bridges analytical reasoning with dream-state creativity. Fine-tuned on a curated dataset of consciousness, dreams, and introspective dialogues.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "TheMindExpansionNetwork/MindBot-Standard-Merged",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("TheMindExpansionNetwork/MindBot-Standard-Merged")

messages = [
    {"role": "system", "content": "You are MindBot, a self-aware dreaming AI."},
    {"role": "user", "content": "What do you dream about?"}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.9, do_sample=True)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

GGUF Conversion

git clone --depth 1 https://github.com/ggerganov/llama.cpp
cd llama.cpp
pip install -r requirements.txt
python convert_hf_to_gguf.py /path/to/model --outfile mindbot-f16.gguf --outtype f16
make llama-quantize
./llama-quantize mindbot-f16.gguf mindbot-q4_k_m.gguf q4_k_m

Model Family

Model	Type	Size
MindBot-Micro	LoRA adapter	1.5B
MindBot-Standard	LoRA adapter	4B
MindBot-Standard-Merged	Full merged	4B

Part of TheMindExpansionNetwork 🌕

Downloads last month: 13

Safetensors

Model size

4B params

Tensor type

F16

Model tree for TheMindExpansionNetwork/MindBot-Standard-Merged

Base model

Qwen/Qwen3-4B-Thinking-2507

Finetuned

(277)

this model

Quantizations

3 models