Text Generation
Transformers
Safetensors
PyTorch
English
modern_dense_mha_gated_ffn_router
custom_code
causal-lm
small-language-model
babylm
strict-small
swiglu
research
Instructions to use AwakeningOS/VISTA-24M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AwakeningOS/VISTA-24M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AwakeningOS/VISTA-24M", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("AwakeningOS/VISTA-24M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AwakeningOS/VISTA-24M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AwakeningOS/VISTA-24M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AwakeningOS/VISTA-24M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AwakeningOS/VISTA-24M
- SGLang
How to use AwakeningOS/VISTA-24M 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 "AwakeningOS/VISTA-24M" \ --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": "AwakeningOS/VISTA-24M", "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 "AwakeningOS/VISTA-24M" \ --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": "AwakeningOS/VISTA-24M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AwakeningOS/VISTA-24M with Docker Model Runner:
docker model run hf.co/AwakeningOS/VISTA-24M
File size: 1,059 Bytes
9287d39 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | {
"model": "VISTA-24M",
"branch": "main",
"word_exposure": 80000000,
"export": {
"status": "PASS",
"checkpoint_sha256": "2b4f0555d6a5e614bd81d09555c008ac8f6310e75a6a3b96378e24fd9d5ff018",
"state": {
"epoch": 8,
"row": 0,
"words": 80000000,
"targets": 129927852,
"step": 8805
},
"kind": "raw",
"continuation": null,
"files": {
"dense.py": "3303f30eda7607136724dfe4124e54010426a5334c42b4227551e02253e6566a",
"config.json": "b6ba8faee226dcacaebc24ec1b7f47159b9fcb799dbdf72c051cb6bbab11be9f",
"configuration_dense.py": "227987f030b3e56ce8c38960ee8d77fa000b355dfe78fcc6c45c05dbe418c544",
"tokenizer.json": "98dfab9eabdd78aed27c025ec1bdbd881172c6339c0b491f9e1612e9a36fcbf8",
"tokenizer_config.json": "ed01997e2c97efc8158f30b9161e79f3b3cd67e8b3b992b968610e84bd96bd31",
"model.safetensors": "ea395c4df82b7ca0f05d4b4d97b2e1102751ce61e66b3e1345645deb8fd6ce43",
"modeling_dense.py": "8fe6cb72985ccccfba423482e1135bee8ad17523b5c91e9c3a261fb3678fd9f4"
}
}
}
|