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: 3,412 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 | {
"status": "PASS",
"scope": "19 exported checkpoint hashes, safetensors headers and vocabulary shapes",
"checkpoints": [
{
"branch": "chck_1M",
"sha256": "d258223385cb167e5880a3a4f67837e4cfedf89085a195f2c3e770591546882f",
"bytes": 97134288,
"status": "PASS"
},
{
"branch": "chck_2M",
"sha256": "dfb17f508191a84d3afac8f93cd8fc8d233317aebae598e236ee8c4fd3916145",
"bytes": 97134288,
"status": "PASS"
},
{
"branch": "chck_3M",
"sha256": "de4cdad20e39f7ef43fc5dad3157434481674c20db3f00a3ab2e1f63f5b31244",
"bytes": 97134288,
"status": "PASS"
},
{
"branch": "chck_4M",
"sha256": "2e9ac1abbcecbc7fa68e0d7b3edb22dbc293bea40d8068671f29b30643611363",
"bytes": 97134288,
"status": "PASS"
},
{
"branch": "chck_5M",
"sha256": "5cd3a31ef7775eb6bf8488604e3a8eb6ebb7cce6aaf7a8050959c3b531463cb9",
"bytes": 97134288,
"status": "PASS"
},
{
"branch": "chck_6M",
"sha256": "e50a8dedbba8e21b7d8cf8cf99cb98840cf99cb0d5a594bbfb016d9c7af83d3c",
"bytes": 97134288,
"status": "PASS"
},
{
"branch": "chck_7M",
"sha256": "b833178fb8d65ca5ce0247a0c05dba577c155e3860eacef26c0a9f88c9ce1563",
"bytes": 97134288,
"status": "PASS"
},
{
"branch": "chck_8M",
"sha256": "8433533b2cfde7a0407781a4d0ef6539c01552f11217e3a821c3b8f5911d2026",
"bytes": 97134288,
"status": "PASS"
},
{
"branch": "chck_9M",
"sha256": "75d3ecfd85d65a0b9bb438ee44febbc83e6d9b078bf0b9952985ee1cbbf62034",
"bytes": 97134288,
"status": "PASS"
},
{
"branch": "chck_10M",
"sha256": "a1e27b3e8649d41b28570daa4e7c467d43f4d719e7c9fd45e71aeebf6f5abb63",
"bytes": 97134288,
"status": "PASS"
},
{
"branch": "chck_20M",
"sha256": "6d1770517ba06c379c8c70b5c320e48430ae9b3f17e5494afc3207aada38b8d2",
"bytes": 97134288,
"status": "PASS"
},
{
"branch": "chck_30M",
"sha256": "b4dcdbaeb594276867318a94dd85438e732777082aa101321f49be0ffd5b8f08",
"bytes": 97134288,
"status": "PASS"
},
{
"branch": "chck_40M",
"sha256": "e5188e47a7061202519a651fa004a76082ee8529c9e43c2b8999c578d2abc367",
"bytes": 97134288,
"status": "PASS"
},
{
"branch": "chck_50M",
"sha256": "70d92bd329c094dc46914c89543816fdf1f16373386f24eda591fb8b4f7dc529",
"bytes": 97134288,
"status": "PASS"
},
{
"branch": "chck_60M",
"sha256": "d7d71056fc9593a508479ed829d5fb7a44df69aed78904cad765c314207064cb",
"bytes": 97134288,
"status": "PASS"
},
{
"branch": "chck_70M",
"sha256": "e6962637d448cb8e4bb2abfe245448115a71770d87458d18c77a0119c3b19a69",
"bytes": 97134288,
"status": "PASS"
},
{
"branch": "chck_80M",
"sha256": "ea395c4df82b7ca0f05d4b4d97b2e1102751ce61e66b3e1345645deb8fd6ce43",
"bytes": 97134288,
"status": "PASS"
},
{
"branch": "chck_90M",
"sha256": "4391e9e7d96d27fb043f3ac2609ba167b654147f60f0e6c49c21bd2fc574eebe",
"bytes": 97134288,
"status": "PASS"
},
{
"branch": "chck_100M",
"sha256": "480bf6a220b463773b7c717a15229930881971c54588b098618f1e5818765b07",
"bytes": 97134288,
"status": "PASS"
}
]
}
|