Instructions to use nkthebass/tinybrainbot-100m-v3-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nkthebass/tinybrainbot-100m-v3-base with Transformers:

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

pipe = pipeline("text-generation", model="nkthebass/tinybrainbot-100m-v3-base")
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("nkthebass/tinybrainbot-100m-v3-base")
model = AutoModelForCausalLM.from_pretrained("nkthebass/tinybrainbot-100m-v3-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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use nkthebass/tinybrainbot-100m-v3-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-100m-v3-base:F16
# Run inference directly in the terminal:
llama cli -hf nkthebass/tinybrainbot-100m-v3-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-100m-v3-base:F16
# Run inference directly in the terminal:
llama cli -hf nkthebass/tinybrainbot-100m-v3-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-100m-v3-base:F16
# Run inference directly in the terminal:
./llama-cli -hf nkthebass/tinybrainbot-100m-v3-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-100m-v3-base:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nkthebass/tinybrainbot-100m-v3-base:F16

Use Docker

docker model run hf.co/nkthebass/tinybrainbot-100m-v3-base:F16

LM Studio
Jan

vLLM

How to use nkthebass/tinybrainbot-100m-v3-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nkthebass/tinybrainbot-100m-v3-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-100m-v3-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nkthebass/tinybrainbot-100m-v3-base:F16

SGLang

How to use nkthebass/tinybrainbot-100m-v3-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-100m-v3-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-100m-v3-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-100m-v3-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-100m-v3-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use nkthebass/tinybrainbot-100m-v3-base with Ollama:
```
ollama run hf.co/nkthebass/tinybrainbot-100m-v3-base:F16
```
Unsloth Desktop
Docker Model Runner
How to use nkthebass/tinybrainbot-100m-v3-base with Docker Model Runner:
```
docker model run hf.co/nkthebass/tinybrainbot-100m-v3-base:F16
```

Lemonade

How to use nkthebass/tinybrainbot-100m-v3-base with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nkthebass/tinybrainbot-100m-v3-base:F16

Run and chat with the model

lemonade run user.tinybrainbot-100m-v3-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-100M-v3-Base

A 100M-parameter (75.5M non-embedding) from-scratch base language model, trained on 25B tokens. Same size and class as SupraLabs/Supra2-100M, and it beats Supra2-100M-Base on 6/7 benchmarks on the official EleutherAI LM-Eval Harness — at a smaller token budget (25B vs 30B).

Architecture: Llama-compatible — hidden 768, 12 layers, 12 heads / 4 KV, FFN 2048, context 1024, vocab 32,000, tied embeddings.
Training: 25B-token WSD pretrain (fineweb-edu / dclm / wikipedia / gutenberg / cosmopedia + a quality anneal), then a short knowledge-focused continuation.

Benchmarks (EleutherAI lm-eval, 0-shot, acc_norm; WinoGrande/MMLU = acc)

Benchmark	This model	Supra2-100M-Base	Supra2-100M-Instruct
ARC-Easy	54.7	47.8	44.4
ARC-Easy (raw acc)	58.6	54.3	51.4
ARC-Challenge	30.1	24.8	24.7
ARC-Challenge (raw acc)	26.6	22.4	22.7
OpenBookQA	34.0	32.0	30.4
OpenBookQA (raw acc)	22.6	19.0	17.2
WinoGrande	53.0	50.7	50.5
PIQA	66.2	65.5	64.4
PIQA (raw acc)	65.7	65.4	64.3
MMLU	25.0	23.3	25.8
HellaSwag	32.6	36.0	35.9
HellaSwag (raw acc)	30.0	31.7	31.5

6/7 vs Supra2-Base and 5/7 vs Supra2-Instruct. HellaSwag is the one benchmark where Supra leads.

Reproduce these numbers

EleutherAI lm-eval-harness v0.4.12, 0-shot, evaluated on the HF repo (do not eval the GGUF — llama.cpp's --multiple-choice path under-reports these tasks; the .hellaswag path is fine):

lm_eval --model hf \
  --model_args pretrained=nkthebass/tinybrainbot-100m-v3-base,dtype=float32 \
  --tasks hellaswag,arc_easy,arc_challenge,openbookqa,winogrande,piqa,mmlu \
  --num_fewshot 0 --batch_size 32

Metrics: acc_norm for HellaSwag / ARC-Easy / ARC-Challenge / OpenBookQA / PIQA; acc for WinoGrande & MMLU.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("nkthebass/tinybrainbot-100m-v3-base")
model = AutoModelForCausalLM.from_pretrained("nkthebass/tinybrainbot-100m-v3-base")
ids = tok("The capital of France is", return_tensors="pt").input_ids
print(tok.decode(model.generate(ids, max_new_tokens=20)[0]))

GGUF

An F16 GGUF is included (tinybrainbot-100m-v3-base-f16.gguf) for llama.cpp / Ollama / LM Studio, with the correct add_space_prefix=false + leading-space template baked in for faithful tokenization.

Limitations

A 100M base model: strong on multiple-choice reasoning for its size, but open-ended generation is limited and can be factually unreliable. For chat use the instruct variant; for arithmetic use the math variant. Not aligned or safety-tuned.

Companion models: instruct · math.

Downloads last month: 319

Safetensors

Model size

0.1B params

Tensor type

F16

Model tree for nkthebass/tinybrainbot-100m-v3-base

Quantizations

1 model

Collection including nkthebass/tinybrainbot-100m-v3-base

TinyBrainBot v3

Collection

Third generation: 100M and 350M base, instruct and math models. • 6 items • Updated 8 days ago