Instructions to use DedeProGames/NanoAndy-230M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DedeProGames/NanoAndy-230M with Transformers:

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

pipe = pipeline("text-generation", model="DedeProGames/NanoAndy-230M")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DedeProGames/NanoAndy-230M")
model = AutoModelForCausalLM.from_pretrained("DedeProGames/NanoAndy-230M", 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 DedeProGames/NanoAndy-230M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DedeProGames/NanoAndy-230M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DedeProGames/NanoAndy-230M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DedeProGames/NanoAndy-230M

SGLang

How to use DedeProGames/NanoAndy-230M 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 "DedeProGames/NanoAndy-230M" \
    --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": "DedeProGames/NanoAndy-230M",
		"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 "DedeProGames/NanoAndy-230M" \
        --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": "DedeProGames/NanoAndy-230M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DedeProGames/NanoAndy-230M with Docker Model Runner:
```
docker model run hf.co/DedeProGames/NanoAndy-230M
```

NanoAndy-230M / README.md

DedeProGames

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	---
	license: other
	license_name: lfm1.0
	license_link: https://huggingface.co/LiquidAI/LFM2.5-230M/blob/main/LICENSE
	language:
	- en
	base_model: LiquidAI/LFM2.5-230M
	datasets:
	- DedeProGames/Andy-4.1-NanoAndy
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- minecraft
	- mindcraft
	- mindcraft-ce
	- agent
	- lfm2
	- edge
	- conversational
	---

	![NanoAndy-230M](logo.png)

	# NanoAndy-230M

	NanoAndy-230M is a compact, full-fine-tuned Minecraft agent model built on [LiquidAI/LFM2.5-230M](https://huggingface.co/LiquidAI/LFM2.5-230M) — Liquid AI's smallest LFM2.5 checkpoint. It is inspired by [Andy-4.1](https://huggingface.co/datasets/Mindcraft-CE/Andy-4.1) and purpose-built to run as the "brain" of a bot in [Mindcraft-CE](https://github.com/mindcraft-ce/mindcraft-ce), the community fork of the open-source Mindcraft platform that lets LLMs control Minecraft characters via Mineflayer.

	At only 230M parameters, this is the smallest model in the NanoAndy line — aimed at the tightest memory and compute budgets: low-end GPUs, CPU-only machines, single-board computers, or running many bots side by side.

	## What makes it "Nano"

	NanoAndy-230M is trained on a stripped-down version of Andy-4.1's conversational data ([DedeProGames/Andy-4.1-NanoAndy](https://huggingface.co/datasets/DedeProGames/Andy-4.1-NanoAndy)):

	- No chain-of-thought. All `<think>...</think>` reasoning traces were removed from the assistant turns. At 230M parameters there's essentially no spare capacity for long internal monologue, so training goes straight to the final in-game response.
	- No function-calling turns. Conversations that used external `tool`-role calls were dropped entirely, keeping the model focused on Mindcraft's native chat/command format instead of a JSON tool-calling schema it wouldn't reliably use at this size.

	The result is a lean, fast, direct-response model rather than a smaller reasoning model.

	## Model Details

	\| \| \|
	\|---\|---\|
	\| Base model \| [LiquidAI/LFM2.5-230M](https://huggingface.co/LiquidAI/LFM2.5-230M) \|
	\| Architecture \| LFM2 (hybrid conv + attention) \|
	\| Parameters \| ~230M (229,693,184) \|
	\| Fine-tuning method \| Full fine-tune (no LoRA/adapters) \|
	\| Context length \| 11,264 tokens \|
	\| Language \| English \|
	\| License \| [LFM Open License v1.0](https://huggingface.co/LiquidAI/LFM2.5-230M/blob/main/LICENSE) (inherited from base model) \|

	## Training

	\| \| \|
	\|---\|---\|
	\| Dataset \| [DedeProGames/Andy-4.1-NanoAndy](https://huggingface.co/datasets/DedeProGames/Andy-4.1-NanoAndy) (1,695 conversations) \|
	\| Framework \| [Unsloth](https://github.com/unslothai/unsloth) \|
	\| Hardware \| 1x NVIDIA T4 (Google Colab) \|
	\| Epochs \| 2 \|
	\| Effective batch size \| 8 (1 x 8 grad. accumulation) \|
	\| Learning rate \| 5e-5, cosine schedule, 15 warmup steps \|
	\| Optimizer \| adamw_8bit \|
	\| Final train loss \| ~0.21 \|

	## Usage

	NanoAndy-230M is meant to be dropped into a Mindcraft-CE [bot profile](https://github.com/mindcraft-ce/mindcraft-ce) (e.g. `andy.json`) as the chat/coding model, served locally through something like LM Studio, llama.cpp, or vLLM. Its small footprint makes it a good fit for running on modest hardware or alongside several other bots at once.

	It also works with standard `transformers`:

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

	model_id = "DedeProGames/NanoAndy-230M"
	model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", dtype="bfloat16")
	tokenizer = AutoTokenizer.from_pretrained(model_id)
	streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

	prompt = "You are a minecraft bot named Andy. A player asks you to gather 4 oak logs."
	input_ids = tokenizer.apply_chat_template(
	[{"role": "user", "content": prompt}],
	add_generation_prompt=True,
	return_tensors="pt",
	tokenize=True,
	)["input_ids"].to(model.device)

	model.generate(
	input_ids,
	do_sample=True,
	temperature=0.3,
	repetition_penalty=1.05,
	max_new_tokens=256,
	streamer=streamer,
	)
	```

	## Limitations

	- Not a reasoning model. With chain-of-thought training data removed, NanoAndy-230M won't show its work — it goes straight to an action/response.
	- No native tool-calling. It was not trained on function-call syntax; it expects Mindcraft's native command/chat format.
	- Very small model. At 230M parameters — the smallest in the NanoAndy line — it will struggle with long-horizon planning, complex builds, and multi-step reasoning more than the 350M variant, and considerably more than full-size Andy-4 models.
	- English only.
	- Narrowly tuned for the Mindcraft agent format — not intended as a general-purpose assistant.

	## Acknowledgements

	- [Liquid AI](https://liquid.ai) for the LFM2.5 base model.
	- [Mindcraft-CE](https://github.com/mindcraft-ce/mindcraft-ce) and the Andy-4.1 dataset authors for the source conversational data and the platform this model targets.
	- [Unsloth](https://github.com/unslothai/unsloth) for the training tooling.