Text Generation
Transformers
TensorBoard
Safetensors
PEFT
qwen2
Trained with AutoTrain
text-generation-inference
conversational
Instructions to use reddest-panda/AutoMAD-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use reddest-panda/AutoMAD-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="reddest-panda/AutoMAD-small") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("reddest-panda/AutoMAD-small") model = AutoModelForCausalLM.from_pretrained("reddest-panda/AutoMAD-small", 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - PEFT
How to use reddest-panda/AutoMAD-small with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use reddest-panda/AutoMAD-small with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "reddest-panda/AutoMAD-small" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "reddest-panda/AutoMAD-small", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/reddest-panda/AutoMAD-small
- SGLang
How to use reddest-panda/AutoMAD-small 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 "reddest-panda/AutoMAD-small" \ --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": "reddest-panda/AutoMAD-small", "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 "reddest-panda/AutoMAD-small" \ --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": "reddest-panda/AutoMAD-small", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use reddest-panda/AutoMAD-small with Docker Model Runner:
docker model run hf.co/reddest-panda/AutoMAD-small
Download training_args.bin from reddest-panda/AutoMAD-small: direct link, hf CLI and curl.
- Browser
- Download file 5.43 kB
-
https://huggingface.co/reddest-panda/AutoMAD-small/resolve/main/training_args.bin
- Command line
-
hf download hf://reddest-panda/AutoMAD-small/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/reddest-panda/AutoMAD-small/resolve/main/training_args.bin
5.43 kB
- Xet hash:
- 1cb0cc06d9ddde68b8682723837b46871e99412b2344c727894341915d78e41e
- Size of remote file:
- 5.43 kB
- SHA256:
- 3f8257bc7403820902b98cdb5ea3159de79669ac337fd44b0cbb01e788d07f33
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