Instructions to use CodeDanCode/CartmenBot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CodeDanCode/CartmenBot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="CodeDanCode/CartmenBot")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("CodeDanCode/CartmenBot") model = AutoModelForCausalLM.from_pretrained("CodeDanCode/CartmenBot", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use CodeDanCode/CartmenBot with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CodeDanCode/CartmenBot" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CodeDanCode/CartmenBot", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/CodeDanCode/CartmenBot
- SGLang
How to use CodeDanCode/CartmenBot 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 "CodeDanCode/CartmenBot" \ --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": "CodeDanCode/CartmenBot", "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 "CodeDanCode/CartmenBot" \ --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": "CodeDanCode/CartmenBot", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use CodeDanCode/CartmenBot with Docker Model Runner:
docker model run hf.co/CodeDanCode/CartmenBot
Download special_tokens_map.json from CodeDanCode/CartmenBot: direct link, hf CLI and curl.
- Browser
- Download file 357 Bytes
-
https://huggingface.co/CodeDanCode/CartmenBot/resolve/main/special_tokens_map.json
- Command line
-
hf download hf://CodeDanCode/CartmenBot/special_tokens_map.json
-
curl -L -o special_tokens_map.json https://huggingface.co/CodeDanCode/CartmenBot/resolve/main/special_tokens_map.json
357 Bytes
| {"bos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "eos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "unk_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}} |