Instructions to use katuni4ka/tiny-random-snowflake with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use katuni4ka/tiny-random-snowflake with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="katuni4ka/tiny-random-snowflake", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("katuni4ka/tiny-random-snowflake", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use katuni4ka/tiny-random-snowflake with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "katuni4ka/tiny-random-snowflake" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "katuni4ka/tiny-random-snowflake", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/katuni4ka/tiny-random-snowflake
- SGLang
How to use katuni4ka/tiny-random-snowflake 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 "katuni4ka/tiny-random-snowflake" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "katuni4ka/tiny-random-snowflake", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "katuni4ka/tiny-random-snowflake" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "katuni4ka/tiny-random-snowflake", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use katuni4ka/tiny-random-snowflake with Docker Model Runner:
docker model run hf.co/katuni4ka/tiny-random-snowflake
| """Tokenization classes for Arctic.""" | |
| from typing import Any, Dict, Optional | |
| from transformers.models.llama import LlamaTokenizer | |
| class ArcticTokenizer(LlamaTokenizer): | |
| def __init__( | |
| self, | |
| vocab_file, | |
| unk_token="<unk>", | |
| bos_token="<s>", | |
| eos_token="</s>", | |
| pad_token=None, | |
| sp_model_kwargs: Optional[Dict[str, Any]] = None, | |
| add_bos_token=True, | |
| add_eos_token=False, | |
| clean_up_tokenization_spaces=False, | |
| use_default_system_prompt=False, | |
| spaces_between_special_tokens=False, | |
| legacy=False, | |
| add_prefix_space=True, | |
| **kwargs, | |
| ): | |
| # Same as LlamaTokenizer except default legacy=False. | |
| super().__init__( | |
| vocab_file, | |
| bos_token=bos_token, | |
| eos_token=eos_token, | |
| unk_token=unk_token, | |
| pad_token=pad_token, | |
| sp_model_kwargs=sp_model_kwargs, | |
| add_bos_token=add_bos_token, | |
| add_eos_token=add_eos_token, | |
| clean_up_tokenization_spaces=clean_up_tokenization_spaces, | |
| use_default_system_prompt=use_default_system_prompt, | |
| spaces_between_special_tokens=spaces_between_special_tokens, | |
| legacy=legacy, | |
| add_prefix_space=add_prefix_space, | |
| **kwargs, | |
| ) | |
| def default_chat_template(self): | |
| """ | |
| This template formats inputs in the standard Arctic format. | |
| """ | |
| return ( | |
| "{% for message in messages %}" | |
| "{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}" | |
| "{% endfor %}" | |
| "{% if add_generation_prompt %}" | |
| "{{ '<|im_start|>assistant\n' }}" | |
| "{% endif %}" | |
| ) | |