Instructions to use nkthebass/tinybrainbot-303mV2-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nkthebass/tinybrainbot-303mV2-instruct with Transformers:

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

pipe = pipeline("text-generation", model="nkthebass/tinybrainbot-303mV2-instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nkthebass/tinybrainbot-303mV2-instruct")
model = AutoModelForCausalLM.from_pretrained("nkthebass/tinybrainbot-303mV2-instruct", 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-303mV2-instruct 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-303mV2-instruct:F16
# Run inference directly in the terminal:
llama cli -hf nkthebass/tinybrainbot-303mV2-instruct:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf nkthebass/tinybrainbot-303mV2-instruct:F16
# Run inference directly in the terminal:
llama cli -hf nkthebass/tinybrainbot-303mV2-instruct: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-303mV2-instruct:F16
# Run inference directly in the terminal:
./llama-cli -hf nkthebass/tinybrainbot-303mV2-instruct: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-303mV2-instruct:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nkthebass/tinybrainbot-303mV2-instruct:F16

Use Docker

docker model run hf.co/nkthebass/tinybrainbot-303mV2-instruct:F16

LM Studio
Jan

vLLM

How to use nkthebass/tinybrainbot-303mV2-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nkthebass/tinybrainbot-303mV2-instruct"
# 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-303mV2-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nkthebass/tinybrainbot-303mV2-instruct:F16

SGLang

How to use nkthebass/tinybrainbot-303mV2-instruct 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-303mV2-instruct" \
    --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-303mV2-instruct",
		"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-303mV2-instruct" \
        --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-303mV2-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use nkthebass/tinybrainbot-303mV2-instruct with Ollama:
```
ollama run hf.co/nkthebass/tinybrainbot-303mV2-instruct:F16
```

Unsloth Studio

How to use nkthebass/tinybrainbot-303mV2-instruct 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-303mV2-instruct 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-303mV2-instruct 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-303mV2-instruct to start chatting

Docker Model Runner
How to use nkthebass/tinybrainbot-303mV2-instruct with Docker Model Runner:
```
docker model run hf.co/nkthebass/tinybrainbot-303mV2-instruct:F16
```

Lemonade

How to use nkthebass/tinybrainbot-303mV2-instruct with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nkthebass/tinybrainbot-303mV2-instruct:F16

Run and chat with the model

lemonade run user.tinybrainbot-303mV2-instruct-F16

List all available models

lemonade list

Atomic Chat

TinyBrainBot 303M V2 — Instruct

A ~303M parameter instruction-tuned language model, trained from scratch on a single 2×GPU workstation. This is the chat/instruct variant; the pretrained foundation is nkthebass/tinybrainbot-303mV2-base.

It's a genuinely small model — think GPT-2-small class — built as a from-scratch LLM project. It knows a fair amount of factual trivia, holds a short chat, greets, gives simple advice, and does basic add/subtract arithmetic with shown work. It is not a general assistant and will confidently hallucinate; see Limitations.

Architecture

Llama-family (RoPE, RMSNorm, SwiGLU, GQA), tied embeddings.


Parameters	~303M
Hidden size	1024
Layers	24
Attention heads	16 (4 KV heads, GQA)
Head dim	64
FFN size	2816 (SwiGLU)
Vocab	32,000 (SentencePiece BPE)
Context length	1024
RoPE theta	10000

Chat format

Single-token role markers, EOS = <|end|>:

<|user|> {message} <|end|> <|assistant|>

The chat template is embedded in tokenizer_config.json (and in the GGUFs), so apply_chat_template / llama-server --jinja handle it for you.

Usage

transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("nkthebass/tinybrainbot-303mV2-instruct")
model = AutoModelForCausalLM.from_pretrained("nkthebass/tinybrainbot-303mV2-instruct")
msgs = [{"role": "user", "content": "What is the capital of France?"}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt")
out = model.generate(ids, max_new_tokens=64, do_sample=False)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))   # -> Paris.

Tip: use greedy (do_sample=False) for factual/arithmetic queries — at this size, sampling wanders. It is also very sensitive to typos (a misspelled "captial" derails it).

llama.cpp / Jan / LM Studio

GGUF files (F16, Q8_0) are included in this repo. In llama.cpp: llama-server -m tinybrainbot-303mV2-instruct-Q8_0.gguf --jinja.

Benchmarks

Standard log-likelihood multiple-choice (lm-eval style, n=200, seed 42). Headline = acc_norm (HellaSwag/ARC/OpenBookQA), acc (WinoGrande/MMLU):

Benchmark	This model	GPT-2-124M	Pythia-410M	random
ARC-Easy	46.0	44	52	25
ARC-Challenge	27.0	22	24	25
OpenBookQA	29.0	29	30	25
HellaSwag	26.0	31	34	25
WinoGrande	47.0	52	53	50
MMLU	21.0	26	25	25

Real signal is on the QA benches (beats GPT-2-124M on ARC-Easy and beats both GPT-2-124M and Pythia-410M on ARC-Challenge). HellaSwag / MMLU / WinoGrande sit at the random floor — the size ceiling of a 303M.

Arithmetic: trained with a verified "show-your-work" math set, so it does addition and subtraction correctly with column steps (e.g. 462 + 23 → shows the ones/tens/hundreds and answers 485). Multiplication and division are still wrong — it attempts the scratchpad but the digits are off.

Training

Pretrained (see the base model) then SFT'd. SFT mix: smoltalk, a synthetic instruction set, multi-step reasoning word problems, verified arithmetic-with-work, multi-turn dialogues, and greetings. Precision fp16, WSD schedule, DDP on 2× Tesla P100.

Limitations

It hallucinates. It's 303M — it does not reliably know facts beyond common trivia, and states wrong answers with full confidence.
Mul/div arithmetic is broken (add/sub is fine).
English only, 1024-token context, greedy-decoding recommended, typo-fragile.
No safety tuning / RLHF. Do not deploy in anything user-facing without your own guardrails.

License

Apache-2.0. Free to use and build on — attribution appreciated. Trained on public/open datasets (FineWeb-Edu, Wikipedia, TinyStories, OpenWebText2, plus synthetic distillation data); please respect the licenses of those upstream sources.

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Safetensors

Model size

0.3B params

Tensor type

F16

Model tree for nkthebass/tinybrainbot-303mV2-instruct

Base model

nkthebass/tinybrainbot-303mV2-base

Quantized

(1)

this model