Instructions to use nkthebass/tinybrainbot-320mV2-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nkthebass/tinybrainbot-320mV2-instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nkthebass/tinybrainbot-320mV2-instruct") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("nkthebass/tinybrainbot-320mV2-instruct") model = AutoModelForCausalLM.from_pretrained("nkthebass/tinybrainbot-320mV2-instruct", 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
- llama.cpp
How to use nkthebass/tinybrainbot-320mV2-instruct 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-320mV2-instruct:F16 # Run inference directly in the terminal: llama cli -hf nkthebass/tinybrainbot-320mV2-instruct:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf nkthebass/tinybrainbot-320mV2-instruct:F16 # Run inference directly in the terminal: llama cli -hf nkthebass/tinybrainbot-320mV2-instruct: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-320mV2-instruct:F16 # Run inference directly in the terminal: ./llama-cli -hf nkthebass/tinybrainbot-320mV2-instruct: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-320mV2-instruct:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf nkthebass/tinybrainbot-320mV2-instruct:F16
Use Docker
docker model run hf.co/nkthebass/tinybrainbot-320mV2-instruct:F16
- LM Studio
- Jan
- vLLM
How to use nkthebass/tinybrainbot-320mV2-instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nkthebass/tinybrainbot-320mV2-instruct" # 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-instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/nkthebass/tinybrainbot-320mV2-instruct:F16
- SGLang
How to use nkthebass/tinybrainbot-320mV2-instruct 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-instruct" \ --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-instruct", "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-instruct" \ --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-instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use nkthebass/tinybrainbot-320mV2-instruct with Ollama:
ollama run hf.co/nkthebass/tinybrainbot-320mV2-instruct:F16
- Unsloth Studio
How to use nkthebass/tinybrainbot-320mV2-instruct 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-320mV2-instruct 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-320mV2-instruct 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-320mV2-instruct to start chatting
- Docker Model Runner
How to use nkthebass/tinybrainbot-320mV2-instruct with Docker Model Runner:
docker model run hf.co/nkthebass/tinybrainbot-320mV2-instruct:F16
- Lemonade
How to use nkthebass/tinybrainbot-320mV2-instruct with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull nkthebass/tinybrainbot-320mV2-instruct:F16
Run and chat with the model
lemonade run user.tinybrainbot-320mV2-instruct-F16
List all available models
lemonade list
- Atomic Chat
to big
not slm
not slm
SLMs: 1k-1B params
LLMs: 1B+
not slm
SLMs: 1k-1B params
LLMs: 1B+
slm is under 1m anything above is llm
There is no official governed model size from an slm to llm . Infact I can call a 1b parameter model an slm if I like I'm not going to because its not really. But I can't tell if your joking or not, in any case this is an slm and you can say whatever you like but if your trying to impose a specific ruleset for it take it up witg someone else.
There is no official governed model size from an slm to llm . Infact I can call a 1b parameter model an slm if I like I'm not going to because its not really. But I can't tell if your joking or not, in any case this is an slm and you can say whatever you like but if your trying to impose a specific ruleset for it take it up witg someone else.
1b is to big like chatgpt size bro fym its slm💀☠️
https://www.microsoft.com/en-us/research/blog/phi-2-the-surprising-power-of-small-language-models/
SLM Small Language Model
go argue with microsoft then know it all
https://www.microsoft.com/en-us/research/blog/phi-2-the-surprising-power-of-small-language-models/
SLM Small Language Model
go argue with microsoft then know it all
why would i read microslop page🧐
If your going to make unfounded claims and argue in a satirical tone please don't waste my time.
If your going to make unfounded claims and argue in a satirical tone please don't waste my time.
you sound like claude lil bro u be using ai to much
There is no official governed model size from an slm to llm . Infact I can call a 1b parameter model an slm if I like I'm not going to because its not really. But I can't tell if your joking or not, in any case this is an slm and you can say whatever you like but if your trying to impose a specific ruleset for it take it up witg someone else.
1b is to big like chatgpt size bro fym its slm💀☠️
That shows that you know NOTHING about AI, ChatGPT haves more than 500 billion parameters, 1B is SMALL.
There is no official governed model size from an slm to llm . Infact I can call a 1b parameter model an slm if I like I'm not going to because its not really. But I can't tell if your joking or not, in any case this is an slm and you can say whatever you like but if your trying to impose a specific ruleset for it take it up witg someone else.
1b is to big like chatgpt size bro fym its slm💀☠️
That shows that you know NOTHING about AI, ChatGPT haves more than 500 billion parameters, 1B is SMALL.
500b is only for moe because its impossible to run transformer 1b is chatgpt medium then normal is like 500m and then slm is 1m and under and super is 10k
There is no official governed model size from an slm to llm . Infact I can call a 1b parameter model an slm if I like I'm not going to because its not really. But I can't tell if your joking or not, in any case this is an slm and you can say whatever you like but if your trying to impose a specific ruleset for it take it up witg someone else.
1b is to big like chatgpt size bro fym its slm💀☠️
That shows that you know NOTHING about AI, ChatGPT haves more than 500 billion parameters, 1B is SMALL.
500b is only for moe because its impossible to run transformer 1b is chatgpt medium then normal is like 500m and then slm is 1m and under and super is 10k
There is no official governed model size from an slm to llm . Infact I can call a 1b parameter model an slm if I like I'm not going to because its not really. But I can't tell if your joking or not, in any case this is an slm and you can say whatever you like but if your trying to impose a specific ruleset for it take it up witg someone else.
1b is to big like chatgpt size bro fym its slm💀☠️
That shows that you know NOTHING about AI, ChatGPT haves more than 500 billion parameters, 1B is SMALL.
500b is only for moe because its impossible to run transformer 1b is chatgpt medium then normal is like 500m and then slm is 1m and under and super is 10k

