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
Release VISTA-24M: model, architecture diagrams, training recipe and evaluation evidence
9287d39 verified Download config.json from AwakeningOS/VISTA-24M: direct link, hf CLI and curl.
- Browser
- Download file 1.07 kB
-
https://huggingface.co/AwakeningOS/VISTA-24M/resolve/main/config.json
- Command line
-
hf download hf://AwakeningOS/VISTA-24M/config.json
-
curl -L -o config.json https://huggingface.co/AwakeningOS/VISTA-24M/resolve/main/config.json
1.07 kB
| { | |
| "auto_map": { | |
| "AutoConfig": "configuration_dense.ModernDenseConfig", | |
| "AutoModel": "modeling_dense.ModernDenseModel", | |
| "AutoModelForCausalLM": "modeling_dense.ModernDenseForCausalLM" | |
| }, | |
| "bos_token_id": 1, | |
| "dense_config": { | |
| "arm": "mha_gated_ffn_router", | |
| "backend": "sdpa", | |
| "dropout": 0.0, | |
| "eps": 1e-06, | |
| "head_dim": 32, | |
| "hidden": 896, | |
| "kv_heads": 8, | |
| "layers": 7, | |
| "q_heads": 8, | |
| "seed": 20260907, | |
| "value_variance": true, | |
| "variance_delivery": "full_width_direct", | |
| "vocab": 16384, | |
| "width": 256 | |
| }, | |
| "eos_token_id": 2, | |
| "hidden_size": 256, | |
| "model_type": "modern_dense_mha_gated_ffn_router", | |
| "pad_token_id": 3, | |
| "tie_word_embeddings": false, | |
| "transformers_version": "5.14.1", | |
| "vocab_size": 16384, | |
| "architectures": [ | |
| "ModernDenseForCausalLM" | |
| ], | |
| "intermediate_size": 896, | |
| "num_hidden_layers": 7, | |
| "num_attention_heads": 8, | |
| "num_key_value_heads": 8, | |
| "head_dim": 32, | |
| "max_position_embeddings": 512, | |
| "rope_theta": 10000.0, | |
| "rms_norm_eps": 1e-06, | |
| "use_cache": false | |
| } | |