Instructions to use nkthebass/TinyBrainBot-303m-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nkthebass/TinyBrainBot-303m-base with Transformers:

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

pipe = pipeline("text-generation", model="nkthebass/TinyBrainBot-303m-base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nkthebass/TinyBrainBot-303m-base")
model = AutoModelForCausalLM.from_pretrained("nkthebass/TinyBrainBot-303m-base", 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

llama.cpp

How to use nkthebass/TinyBrainBot-303m-base with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf nkthebass/TinyBrainBot-303m-base:F16
# Run inference directly in the terminal:
llama cli -hf nkthebass/TinyBrainBot-303m-base:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf nkthebass/TinyBrainBot-303m-base:F16
# Run inference directly in the terminal:
llama cli -hf nkthebass/TinyBrainBot-303m-base:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf nkthebass/TinyBrainBot-303m-base:F16
# Run inference directly in the terminal:
./llama-cli -hf nkthebass/TinyBrainBot-303m-base:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf nkthebass/TinyBrainBot-303m-base:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nkthebass/TinyBrainBot-303m-base:F16

Use Docker

docker model run hf.co/nkthebass/TinyBrainBot-303m-base:F16

LM Studio
Jan

vLLM

How to use nkthebass/TinyBrainBot-303m-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nkthebass/TinyBrainBot-303m-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nkthebass/TinyBrainBot-303m-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nkthebass/TinyBrainBot-303m-base:F16

SGLang

How to use nkthebass/TinyBrainBot-303m-base 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 "nkthebass/TinyBrainBot-303m-base" \
    --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": "nkthebass/TinyBrainBot-303m-base",
		"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 "nkthebass/TinyBrainBot-303m-base" \
        --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": "nkthebass/TinyBrainBot-303m-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use nkthebass/TinyBrainBot-303m-base with Ollama:
```
ollama run hf.co/nkthebass/TinyBrainBot-303m-base:F16
```

Unsloth Studio

How to use nkthebass/TinyBrainBot-303m-base 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 nkthebass/TinyBrainBot-303m-base 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 nkthebass/TinyBrainBot-303m-base to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for nkthebass/TinyBrainBot-303m-base to start chatting

Docker Model Runner
How to use nkthebass/TinyBrainBot-303m-base with Docker Model Runner:
```
docker model run hf.co/nkthebass/TinyBrainBot-303m-base:F16
```

Lemonade

How to use nkthebass/TinyBrainBot-303m-base with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nkthebass/TinyBrainBot-303m-base:F16

Run and chat with the model

lemonade run user.TinyBrainBot-303m-base-F16

List all available models

lemonade list

Atomic Chat

TinyBrainBot 303M — Base

A 303M-parameter, LLaMA-architecture language model trained from scratch on a home server (2× NVIDIA Tesla P100). This is the base (pretrained) model — it has not been instruction-tuned, so it completes text rather than following chat instructions.

👉 For the chat/assistant version, see TinyBrainBot 303M Instruct.

Model details


Parameters	~303M
Architecture	LLaMA-style decoder (RoPE, RMSNorm, SwiGLU, pre-norm)
Layers	24
Hidden size	1024
Attention heads	16 (GQA, 4 KV heads)
Head dim	64
FFN size	2816
Vocab	32,000 (custom SentencePiece BPE)
Context length	1024
Tied embeddings	Yes
Precision	fp16

It re-expresses cleanly as a LlamaForCausalLM, so it loads with standard 🤗 Transformers.

Training

Pretrained on a mix of open English text plus synthetic distillation data:

Web / educational text: FineWeb-Edu, English Wikipedia, OpenWebText2
Narrative: TinyStories
Math/reasoning: Orca-Math
Synthetic fact distillation: Q&A and short-fact data generated by a stronger teacher model (Ling 2.6 Flash) to densify factual coverage.

Total training tokens are on the order of a few billion — far fewer than models like SmolLM/Pythia (hundreds of billions). This is a hobbyist/research model; capability scales with tokens, and this one is intentionally token-limited. (4.7B)

Intended use

A starting point for continued pretraining or fine-tuning at small scale.
Text completion and research on small language models.
Not for chat/instruction following — use the Instruct version for that.

Limitations

Shallow, fragile factual knowledge. It knows common facts but is sensitive to phrasing and capitalization (e.g. "France" vs "france" can flip the answer).
Weak at reasoning and math; roughly random on knowledge benchmarks like MMLU (typical for this size).
Will produce fluent plausible-but-wrong text confidently. Pair with retrieval (RAG) for anything factual.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

tok = AutoTokenizer.from_pretrained("your-username/tinybrainbot-303m-base")
model = AutoModelForCausalLM.from_pretrained("your-username/tinybrainbot-303m-base", torch_dtype=torch.float16)

ids = tok("The water cycle is the process by which", return_tensors="pt").input_ids
out = model.generate(ids, max_new_tokens=40, do_sample=True, temperature=0.6, top_p=0.9, repetition_penalty=1.2)
print(tok.decode(out[0], skip_special_tokens=True))

License

Apache-2.0 (change if you prefer a different license).

Downloads last month: 596

Safetensors

Model size

0.3B params

Tensor type

F16