Instructions to use ReallyNotMe/model16bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ReallyNotMe/model16bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ReallyNotMe/model16bit") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ReallyNotMe/model16bit") model = AutoModelForCausalLM.from_pretrained("ReallyNotMe/model16bit") 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 ReallyNotMe/model16bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ReallyNotMe/model16bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ReallyNotMe/model16bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ReallyNotMe/model16bit
- SGLang
How to use ReallyNotMe/model16bit 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 "ReallyNotMe/model16bit" \ --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": "ReallyNotMe/model16bit", "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 "ReallyNotMe/model16bit" \ --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": "ReallyNotMe/model16bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio
How to use ReallyNotMe/model16bit 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 ReallyNotMe/model16bit 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 ReallyNotMe/model16bit to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ReallyNotMe/model16bit to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="ReallyNotMe/model16bit", max_seq_length=2048, ) - Docker Model Runner
How to use ReallyNotMe/model16bit with Docker Model Runner:
docker model run hf.co/ReallyNotMe/model16bit
File size: 1,226 Bytes
9842855 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | {
"</tool_call>": 145124,
"<tool_call>": 145123,
"<|box_end|>": 145115,
"<|box_start|>": 145114,
"<|endoftext|>": 145109,
"<|file_sep|>": 145130,
"<|fim_middle|>": 145126,
"<|fim_pad|>": 145128,
"<|fim_prefix|>": 145125,
"<|fim_suffix|>": 145127,
"<|free_token10|>": 145140,
"<|free_token11|>": 145141,
"<|free_token12|>": 145142,
"<|free_token13|>": 145143,
"<|free_token14|>": 145144,
"<|free_token15|>": 145145,
"<|free_token16|>": 145146,
"<|free_token17|>": 145147,
"<|free_token18|>": 145148,
"<|free_token19|>": 145149,
"<|free_token1|>": 145131,
"<|free_token20|>": 145150,
"<|free_token21|>": 145151,
"<|free_token2|>": 145132,
"<|free_token3|>": 145133,
"<|free_token4|>": 145134,
"<|free_token5|>": 145135,
"<|free_token6|>": 145136,
"<|free_token7|>": 145137,
"<|free_token8|>": 145138,
"<|free_token9|>": 145139,
"<|im_end|>": 145111,
"<|im_start|>": 145110,
"<|image_pad|>": 145121,
"<|object_ref_end|>": 145113,
"<|object_ref_start|>": 145112,
"<|quad_end|>": 145117,
"<|quad_start|>": 145116,
"<|repo_name|>": 145129,
"<|video_pad|>": 145122,
"<|vision_end|>": 145119,
"<|vision_pad|>": 145120,
"<|vision_start|>": 145118
}
|