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: 2,789 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 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 | [
{
"words_m": 10,
"blimp_filtered": 65.33,
"supplement_filtered": 57.34,
"ewok_filtered": 49.8,
"entity_tracking": 17.78,
"comps": 49.82,
"global_piqa_parallel": 23.3,
"global_piqa_nonparallel": 43.0,
"six_task_mean": 45.53666666666666
},
{
"words_m": 20,
"blimp_filtered": 67.45,
"supplement_filtered": 56.46,
"ewok_filtered": 49.97,
"entity_tracking": 18.26,
"comps": 51.01,
"global_piqa_parallel": 20.39,
"global_piqa_nonparallel": 48.0,
"six_task_mean": 46.22416666666667
},
{
"words_m": 30,
"blimp_filtered": 67.11,
"supplement_filtered": 57.17,
"ewok_filtered": 50.61,
"entity_tracking": 18.57,
"comps": 51.41,
"global_piqa_parallel": 23.3,
"global_piqa_nonparallel": 46.0,
"six_task_mean": 46.586666666666666
},
{
"words_m": 40,
"blimp_filtered": 68.0,
"supplement_filtered": 56.46,
"ewok_filtered": 51.19,
"entity_tracking": 17.98,
"comps": 51.41,
"global_piqa_parallel": 22.33,
"global_piqa_nonparallel": 48.0,
"six_task_mean": 46.70083333333333
},
{
"words_m": 50,
"blimp_filtered": 68.62,
"supplement_filtered": 55.74,
"ewok_filtered": 51.17,
"entity_tracking": 18.58,
"comps": 51.52,
"global_piqa_parallel": 26.21,
"global_piqa_nonparallel": 46.0,
"six_task_mean": 46.95583333333334
},
{
"words_m": 60,
"blimp_filtered": 67.84,
"supplement_filtered": 55.73,
"ewok_filtered": 51.71,
"entity_tracking": 18.56,
"comps": 51.86,
"global_piqa_parallel": 22.33,
"global_piqa_nonparallel": 45.0,
"six_task_mean": 46.560833333333335
},
{
"words_m": 70,
"blimp_filtered": 67.67,
"supplement_filtered": 55.8,
"ewok_filtered": 51.83,
"entity_tracking": 17.97,
"comps": 51.48,
"global_piqa_parallel": 27.18,
"global_piqa_nonparallel": 46.0,
"six_task_mean": 46.89000000000001
},
{
"words_m": 80,
"blimp_filtered": 67.73,
"supplement_filtered": 58.41,
"ewok_filtered": 52.83,
"entity_tracking": 18.56,
"comps": 51.32,
"global_piqa_parallel": 28.16,
"global_piqa_nonparallel": 51.0,
"six_task_mean": 48.071666666666665
},
{
"words_m": 90,
"blimp_filtered": 67.34,
"supplement_filtered": 55.9,
"ewok_filtered": 51.99,
"entity_tracking": 18.16,
"comps": 51.45,
"global_piqa_parallel": 23.3,
"global_piqa_nonparallel": 47.0,
"six_task_mean": 46.665
},
{
"words_m": 100,
"blimp_filtered": 67.41,
"supplement_filtered": 56.27,
"ewok_filtered": 51.89,
"entity_tracking": 18.26,
"comps": 51.25,
"global_piqa_parallel": 24.27,
"global_piqa_nonparallel": 46.0,
"six_task_mean": 46.70250000000001
}
]
|