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)# 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
Release VISTA-24M: model, architecture diagrams, training recipe and evaluation evidence
9287d39 verified | { | |
| "status": "PASS", | |
| "identity": { | |
| "checkpoint_sha256": "67623618f45ab3af7831203e9c614e3e9bd95d6dac0cc2fe439feb4f57b365bc", | |
| "freeze_sha256": "cc51071f5efb37a9362761cd27e33526f37cab43e1932787f5a1d4d25495435e", | |
| "level": "full" | |
| }, | |
| "accuracy": { | |
| "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 | |
| }, | |
| "margins": { | |
| "blimp_filtered": { | |
| "mean": 1.810616910229645, | |
| "n": 59875 | |
| }, | |
| "supplement_filtered": { | |
| "mean": 3.5167688769643544, | |
| "n": 5218 | |
| }, | |
| "ewok_filtered": { | |
| "mean": 0.004348254134943555, | |
| "n": 7618 | |
| }, | |
| "entity_tracking": { | |
| "mean": -3.0808834038741253, | |
| "n": 6780 | |
| }, | |
| "comps": { | |
| "mean": 0.11904922111877664, | |
| "n": 91028 | |
| }, | |
| "global_piqa_parallel": { | |
| "mean": -1.3787284205071886, | |
| "n": 103 | |
| }, | |
| "global_piqa_nonparallel": { | |
| "mean": -0.02765625, | |
| "n": 100 | |
| } | |
| }, | |
| "reading": { | |
| "EYE TRACKING SCORE": 0.47, | |
| "SELF-PACED READING SCORE": 0.57 | |
| }, | |
| "six_axis": 46.70083333333333 | |
| } | |