Instructions to use nkthebass/tinybrainbot-320mV2-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nkthebass/tinybrainbot-320mV2-base with Transformers:

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

pipe = pipeline("text-generation", model="nkthebass/tinybrainbot-320mV2-base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nkthebass/tinybrainbot-320mV2-base")
model = AutoModelForCausalLM.from_pretrained("nkthebass/tinybrainbot-320mV2-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

vLLM

How to use nkthebass/tinybrainbot-320mV2-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nkthebass/tinybrainbot-320mV2-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-320mV2-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nkthebass/tinybrainbot-320mV2-base

SGLang

How to use nkthebass/tinybrainbot-320mV2-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-320mV2-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-320mV2-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-320mV2-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-320mV2-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nkthebass/tinybrainbot-320mV2-base with Docker Model Runner:
```
docker model run hf.co/nkthebass/tinybrainbot-320mV2-base
```

tinybrainbot-320mV2-base / tokenizer_config.json

nkthebass

Fix safetensors tokenizer: fast PreTrainedTokenizerFast w/ nmt_nfkc normalizer + normalized specials + leading-space template (matches native)

d8c96ac verified 1 day ago

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449 Bytes

	{
	"tokenizer_class": "PreTrainedTokenizerFast",
	"bos_token": "<s>",
	"eos_token": "<\|end\|>",
	"unk_token": "<unk>",
	"pad_token": "<pad>",
	"add_bos_token": false,
	"add_eos_token": false,
	"clean_up_tokenization_spaces": false,
	"chat_template": "{{ ' ' }}{%- for m in messages -%}{{- '<\|' + m['role'] + '\|> ' + m['content'] + ' <\|end\|> ' -}}{%- endfor -%}{%- if add_generation_prompt -%}{{- '<\|assistant\|>' -}}{%- endif -%}"
	}