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

Libraries

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

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

pipe = pipeline("text-generation", model="nkthebass/tinybrainbot-303mV2-base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nkthebass/tinybrainbot-303mV2-base")
model = AutoModelForCausalLM.from_pretrained("nkthebass/tinybrainbot-303mV2-base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use nkthebass/tinybrainbot-303mV2-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-303mV2-base:F16
# Run inference directly in the terminal:
llama cli -hf nkthebass/tinybrainbot-303mV2-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-303mV2-base:F16
# Run inference directly in the terminal:
llama cli -hf nkthebass/tinybrainbot-303mV2-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-303mV2-base:F16
# Run inference directly in the terminal:
./llama-cli -hf nkthebass/tinybrainbot-303mV2-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-303mV2-base:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nkthebass/tinybrainbot-303mV2-base:F16

Use Docker

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

LM Studio
Jan

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nkthebass/tinybrainbot-303mV2-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nkthebass/tinybrainbot-303mV2-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

How to use nkthebass/tinybrainbot-303mV2-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-303mV2-base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nkthebass/tinybrainbot-303mV2-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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-base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nkthebass/tinybrainbot-303mV2-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

Unsloth Studio

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

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Atomic Chat
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

TinyBrainBot 303M V2 — Base

A ~303M parameter language model pretrained from scratch on a single 2×GPU workstation. This is the base (completion) model — no instruction tuning. For the chat variant, see nkthebass/tinybrainbot-303mV2-instruct.

Use this if you want a small English foundation model to continue text or fine-tune yourself.

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

Usage

transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("nkthebass/tinybrainbot-303mV2-base")
model = AutoModelForCausalLM.from_pretrained("nkthebass/tinybrainbot-303mV2-base")
ids = tok("Once upon a time, in a small village", return_tensors="pt").input_ids
out = model.generate(ids, max_new_tokens=40, do_sample=True, top_p=0.9, temperature=0.8)
print(tok.decode(out[0], skip_special_tokens=True))

llama.cpp / Jan / LM Studio

GGUF files (F16, Q8_0) are included in this repo.

Training data

Pretrained (fp16, WSD schedule, DDP on 2× Tesla P100) on a mix of:

FineWeb-Edu (10BT sample) — filtered educational web text
Wikipedia (English)
TinyStories v2 — simple synthetic stories (grammar/coherence)
OpenWebText2
Orca-Math — grade-school math word problems
Synthetic distillation sets (generated by a larger teacher): short-answer facts + longer general Q&A

Note: because the pretraining mix included some chat-formatted synthetic Q&A, the base will occasionally continue a completion into <|user|>/<|assistant|>-style turns. That's expected for this base; the instruct model is the one tuned to actually chat.

Benchmarks

Multiple-choice log-likelihood scoring puts the family at roughly GPT-2-124M class on QA benchmarks (ARC-Easy ~46, ARC-Challenge ~27, beating GPT-2-124M on both), and at the random floor on HellaSwag/MMLU/WinoGrande — the size ceiling of a 303M. Full numbers are on the instruct card (multiple-choice scores barely move between base and instruct).

Limitations

303M parameters — it hallucinates and knows only common facts.
English only, 1024-token context.
No instruction tuning, no safety tuning. This is a raw base model.

License

Apache-2.0. Free to use and build on — attribution appreciated. Trained on public/open datasets (FineWeb-Edu, Wikipedia, TinyStories, OpenWebText2, Orca-Math, 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-base

Quantizations

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