Text Generation
Transformers
Safetensors
English
llama
tiny
small-language-model
pretrained
wisp
text-generation-inference
Instructions to use DedeProGames/Wisp-15M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DedeProGames/Wisp-15M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="DedeProGames/Wisp-15M")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("DedeProGames/Wisp-15M") model = AutoModelForCausalLM.from_pretrained("DedeProGames/Wisp-15M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use DedeProGames/Wisp-15M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DedeProGames/Wisp-15M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DedeProGames/Wisp-15M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/DedeProGames/Wisp-15M
- SGLang
How to use DedeProGames/Wisp-15M 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 "DedeProGames/Wisp-15M" \ --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": "DedeProGames/Wisp-15M", "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 "DedeProGames/Wisp-15M" \ --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": "DedeProGames/Wisp-15M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use DedeProGames/Wisp-15M with Docker Model Runner:
docker model run hf.co/DedeProGames/Wisp-15M
Download training_summary.json from DedeProGames/Wisp-15M: direct link, hf CLI and curl.
- Browser
- Download file 1.63 kB
-
https://huggingface.co/DedeProGames/Wisp-15M/resolve/main/training_summary.json
- Command line
-
hf download hf://DedeProGames/Wisp-15M/training_summary.json
-
curl -L -o training_summary.json https://huggingface.co/DedeProGames/Wisp-15M/resolve/main/training_summary.json
1.63 kB
| { | |
| "size": "15M", | |
| "params": 15531840, | |
| "config": { | |
| "vocab_size": 8192, | |
| "hidden_size": 320, | |
| "intermediate_size": 864, | |
| "num_hidden_layers": 12, | |
| "num_attention_heads": 10, | |
| "num_key_value_heads": 2, | |
| "max_position_embeddings": 2048, | |
| "rope_theta": 10000.0, | |
| "rms_norm_eps": 1e-05, | |
| "initializer_range": 0.02 | |
| }, | |
| "tokens": 2999975936, | |
| "steps": 11444, | |
| "batch_tokens": 262144, | |
| "seq_len": 2048, | |
| "opt": "muon", | |
| "lr": 0.007, | |
| "wd": 0.1, | |
| "warmup_frac": 0.01, | |
| "decay_frac": 0.2, | |
| "gpu": "NVIDIA L4", | |
| "train_minutes": 372.6, | |
| "final_val": { | |
| "dclm": 3.1669, | |
| "finemath": 2.2753, | |
| "fineweb": 2.9144, | |
| "stack": 1.4606, | |
| "avg": 2.4543 | |
| }, | |
| "samples": [ | |
| "The meaning of life is that the person who lives in a certain place will be able to live with his or her own needs. The person who lives in an area where he/she has no need for food and drinks, can live without food.\nIn addition", | |
| "Once upon a time, the people of the country were in their own hands. The people of the country were not able to get any help from them. They had no idea what they wanted and could not find anything else.\nThe people of the country were not able", | |
| "def is_prime(n): print(prime)\nprint(\"Prime number: \", n)\n\n# Prime numbers are not prime\nprint(prime)\nprint(prime)\nprint(prime)\n\n# Prime numbers are not prime\nprint(", | |
| "The derivative of x^2 is 1/x^3. The derivative of y^2 is 0, which means that the derivative of y is 0.\n\n## What is the derivative of x?\n\nThe derivative of a function is defined as" | |
| ], | |
| "parity_max_diff": 2.193450927734375e-05 | |
| } |