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: 1,186 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 | {
"status": "PASS",
"identity": {
"checkpoint_sha256": "d749187ce9da4fa809f95162b00dfcdfdaa64951734eb0552777af900bfc8501",
"freeze_sha256": "cc51071f5efb37a9362761cd27e33526f37cab43e1932787f5a1d4d25495435e",
"level": "full"
},
"accuracy": {
"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
},
"margins": {
"blimp_filtered": {
"mean": 2.012278705636743,
"n": 59875
},
"supplement_filtered": {
"mean": 4.214114603296282,
"n": 5218
},
"ewok_filtered": {
"mean": 0.02607311630349173,
"n": 7618
},
"entity_tracking": {
"mean": -3.719889705327861,
"n": 6780
},
"comps": {
"mean": 0.1555960116667399,
"n": 91028
},
"global_piqa_parallel": {
"mean": -1.376444190058314,
"n": 103
},
"global_piqa_nonparallel": {
"mean": -0.0225,
"n": 100
}
},
"reading": {
"EYE TRACKING SCORE": 0.19,
"SELF-PACED READING SCORE": 0.29
},
"six_axis": 46.70250000000001
}
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