Text Generation
Transformers
Chinese
English
stellarai
multimodal
tiny-llm
causal-lm
vision
cpu-friendly
custom_code
Instructions to use AMT-Studio/StellarAI-1-beta-0.05b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AMT-Studio/StellarAI-1-beta-0.05b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AMT-Studio/StellarAI-1-beta-0.05b", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("AMT-Studio/StellarAI-1-beta-0.05b", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AMT-Studio/StellarAI-1-beta-0.05b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AMT-Studio/StellarAI-1-beta-0.05b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AMT-Studio/StellarAI-1-beta-0.05b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AMT-Studio/StellarAI-1-beta-0.05b
- SGLang
How to use AMT-Studio/StellarAI-1-beta-0.05b 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 "AMT-Studio/StellarAI-1-beta-0.05b" \ --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": "AMT-Studio/StellarAI-1-beta-0.05b", "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 "AMT-Studio/StellarAI-1-beta-0.05b" \ --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": "AMT-Studio/StellarAI-1-beta-0.05b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AMT-Studio/StellarAI-1-beta-0.05b with Docker Model Runner:
docker model run hf.co/AMT-Studio/StellarAI-1-beta-0.05b
| { | |
| "architectures": ["StellarAIForCausalLM"], | |
| "model_type": "stellarai", | |
| "auto_map": { | |
| "AutoConfig": "configuration_stellarai.StellarAIConfig", | |
| "AutoModelForCausalLM": "modeling_stellarai.StellarAIForCausalLM", | |
| "AutoTokenizer": "tokenization_stellarai.StellarAITokenizer" | |
| }, | |
| "d_model": 384, | |
| "num_hidden_layers": 4, | |
| "num_attention_heads": 6, | |
| "intermediate_size": 1536, | |
| "hidden_act": "gelu", | |
| "max_position_embeddings": 1024, | |
| "vocab_size": 32000, | |
| "dropout": 0.1, | |
| "layer_norm_eps": 1e-6, | |
| "rope_theta": 10000.0, | |
| "use_cache": true, | |
| "pad_token_id": 0, | |
| "bos_token_id": 1, | |
| "eos_token_id": 2, | |
| "unk_token_id": 3, | |
| "sep_token_id": 6, | |
| "vision_token_id": 7, | |
| "boi_token_id": 8, | |
| "eoi_token_id": 9, | |
| "vision_cfg": { | |
| "image_size": 224, | |
| "patch_size": 16, | |
| "num_channels": 3, | |
| "cnn_channels": [24, 48, 96, 192], | |
| "vision_num_layers": 2, | |
| "vision_num_heads": 6, | |
| "vision_ff_dim": 768, | |
| "vision_num_patches": 196 | |
| }, | |
| "mm_fusion_cfg": { | |
| "fusion_num_layers": 1, | |
| "fusion_num_heads": 6, | |
| "fusion_ff_dim": 1536 | |
| }, | |
| "lm_head_bias": true, | |
| "use_weight_tying": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.30.0" | |
| } | |