Instructions to use nkthebass/tinybrainbot-360m-a75m-v5.1-python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nkthebass/tinybrainbot-360m-a75m-v5.1-python with Transformers:

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

pipe = pipeline("text-generation", model="nkthebass/tinybrainbot-360m-a75m-v5.1-python")
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-360m-a75m-v5.1-python")
model = AutoModelForCausalLM.from_pretrained("nkthebass/tinybrainbot-360m-a75m-v5.1-python", 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-360m-a75m-v5.1-python 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-360m-a75m-v5.1-python:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nkthebass/tinybrainbot-360m-a75m-v5.1-python:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf nkthebass/tinybrainbot-360m-a75m-v5.1-python:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nkthebass/tinybrainbot-360m-a75m-v5.1-python:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf nkthebass/tinybrainbot-360m-a75m-v5.1-python:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf nkthebass/tinybrainbot-360m-a75m-v5.1-python:Q4_K_M

Use Docker

docker model run hf.co/nkthebass/tinybrainbot-360m-a75m-v5.1-python:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use nkthebass/tinybrainbot-360m-a75m-v5.1-python with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nkthebass/tinybrainbot-360m-a75m-v5.1-python"
# 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-360m-a75m-v5.1-python",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nkthebass/tinybrainbot-360m-a75m-v5.1-python:Q4_K_M

SGLang

How to use nkthebass/tinybrainbot-360m-a75m-v5.1-python 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-360m-a75m-v5.1-python" \
    --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-360m-a75m-v5.1-python",
		"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-360m-a75m-v5.1-python" \
        --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-360m-a75m-v5.1-python",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use nkthebass/tinybrainbot-360m-a75m-v5.1-python with Ollama:
```
ollama run hf.co/nkthebass/tinybrainbot-360m-a75m-v5.1-python:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use nkthebass/tinybrainbot-360m-a75m-v5.1-python with Docker Model Runner:
```
docker model run hf.co/nkthebass/tinybrainbot-360m-a75m-v5.1-python:Q4_K_M
```

Lemonade

How to use nkthebass/tinybrainbot-360m-a75m-v5.1-python with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nkthebass/tinybrainbot-360m-a75m-v5.1-python:Q4_K_M

Run and chat with the model

lemonade run user.tinybrainbot-360m-a75m-v5.1-python-Q4_K_M

List all available models

lemonade list

Atomic Chat

tinybrainbot-360m-a75m-v5.1-python

File size: 918 Bytes

edd5966

{
  "tokenizer_class": "PreTrainedTokenizerFast",
  "bos_token": "<s>",
  "eos_token": "<|end|>",
  "unk_token": "<unk>",
  "add_bos_token": false,
  "add_eos_token": false,
  "clean_up_tokenization_spaces": false,
  "chat_template": "{{ ' ' }}{%- for m in messages -%}{{- '<|' + m['role'] + '|>\\n' + m['content'] + '\\n<|end|>\\n' -}}{%- endfor -%}{%- if add_generation_prompt -%}{{- '<|assistant|>\\n' -}}{%- endif -%}",
  "added_tokens_decoder": {
    "25": {
      "content": "\n",
      "lstrip": false,
      "normalized": false,
      "rstrip": false,
      "single_word": false,
      "special": false
    },
    "24": {
      "content": "    ",
      "lstrip": false,
      "normalized": false,
      "rstrip": false,
      "single_word": false,
      "special": false
    }
  },
  "model_max_length": 2048,
  "model_input_names": [
    "input_ids",
    "attention_mask"
  ]
}