Text Generation
Transformers
Safetensors
GGUF
English
mixtral
tinybrainbot
mixture-of-experts
Mixture of Experts
small-language-model
from-scratch
code
python
conversational
Eval Results (legacy)
text-generation-inference
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
- Transformers
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
Download tokenizer_config.json from nkthebass/tinybrainbot-360m-a75m-v5.1-python: direct link, hf CLI and curl.
- Browser
- Download file 918 Bytes
-
https://huggingface.co/nkthebass/tinybrainbot-360m-a75m-v5.1-python/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://nkthebass/tinybrainbot-360m-a75m-v5.1-python/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/nkthebass/tinybrainbot-360m-a75m-v5.1-python/resolve/main/tokenizer_config.json
918 Bytes
| { | |
| "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" | |
| ] | |
| } |