Text Generation
Transformers
Safetensors
English
lfm3
llm
language-model
open-source
instruction-following
conversational
custom_code
Instructions to use cosmicstack/cosmic-lightning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cosmicstack/cosmic-lightning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="cosmicstack/cosmic-lightning", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("cosmicstack/cosmic-lightning", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use cosmicstack/cosmic-lightning with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cosmicstack/cosmic-lightning" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cosmicstack/cosmic-lightning", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/cosmicstack/cosmic-lightning
- SGLang
How to use cosmicstack/cosmic-lightning 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 "cosmicstack/cosmic-lightning" \ --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": "cosmicstack/cosmic-lightning", "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 "cosmicstack/cosmic-lightning" \ --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": "cosmicstack/cosmic-lightning", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use cosmicstack/cosmic-lightning with Docker Model Runner:
docker model run hf.co/cosmicstack/cosmic-lightning
Download tokenizer_config.json from cosmicstack/cosmic-lightning: direct link, hf CLI and curl.
- Browser
- Download file 1.86 kB
-
https://huggingface.co/cosmicstack/cosmic-lightning/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://cosmicstack/cosmic-lightning/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/cosmicstack/cosmic-lightning/resolve/main/tokenizer_config.json
1.86 kB
| { | |
| "audio_out_token": "<|audio_out|>", | |
| "audio_token": "<|audio|>", | |
| "backend": "tokenizers", | |
| "bos_token": "<|startoftext|>", | |
| "eos_token": "<|im_end|>", | |
| "extra_special_tokens": [], | |
| "image_token": "<|image|>", | |
| "is_local": false, | |
| "local_files_only": false, | |
| "model_max_length": 1000000000000000019884624838656, | |
| "model_specific_special_tokens": { | |
| "audio_out_token": "<|audio_out|>", | |
| "audio_token": "<|audio|>", | |
| "image_token": "<|image|>", | |
| "video_token": "<|video|>", | |
| "vision_end_token": "<|vision_end|>", | |
| "vision_start_token": "<|vision_start|>" | |
| }, | |
| "pad_token": "<|pad|>", | |
| "response_template": { | |
| "defaults": { | |
| "role": "assistant" | |
| }, | |
| "fields": { | |
| "content": { | |
| "close_pattern": "<\\|im_end\\|>\\s*", | |
| "content": "text" | |
| }, | |
| "reasoning_content": { | |
| "close_pattern": "</think>\\s*", | |
| "content": "text", | |
| "open_pattern": "<think>\\n?" | |
| }, | |
| "tool_calls": { | |
| "close_pattern": "</tool_call>\\s*", | |
| "content": "xml-inline", | |
| "content_args": { | |
| "tag_pattern": "<parameter=(?P<key>[^>\\n]+)>\\n?(?P<value>.*?)\\n?</parameter>(?=\\s*(?:<parameter=|</function>\\s*\\Z|\\Z))", | |
| "value_parser": { | |
| "args": { | |
| "strip": false | |
| }, | |
| "name": "text" | |
| } | |
| }, | |
| "open_pattern": "<tool_call>\\s*<function=(?P<name>[^\\n>]+)>", | |
| "repeats": true, | |
| "transform": { | |
| "function": { | |
| "arguments": "{content}", | |
| "name": "{name}" | |
| }, | |
| "type": "function" | |
| } | |
| } | |
| }, | |
| "start_anchor_pattern": "<\\|im_start\\|>assistant\\n" | |
| }, | |
| "tokenizer_class": "TokenizersBackend", | |
| "video_token": "<|video|>", | |
| "vision_end_token": "<|vision_end|>", | |
| "vision_start_token": "<|vision_start|>" | |
| } | |