Text Generation
Transformers
Safetensors
English
gpts14m
language-model
transformer
rope
swiglu
custom-architecture
custom-tokenizer
xgqa
custom_code
Instructions to use AxiomicLabs/GPT-S-1.4M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AxiomicLabs/GPT-S-1.4M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AxiomicLabs/GPT-S-1.4M", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("AxiomicLabs/GPT-S-1.4M", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AxiomicLabs/GPT-S-1.4M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AxiomicLabs/GPT-S-1.4M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AxiomicLabs/GPT-S-1.4M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AxiomicLabs/GPT-S-1.4M
- SGLang
How to use AxiomicLabs/GPT-S-1.4M 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 "AxiomicLabs/GPT-S-1.4M" \ --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": "AxiomicLabs/GPT-S-1.4M", "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 "AxiomicLabs/GPT-S-1.4M" \ --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": "AxiomicLabs/GPT-S-1.4M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AxiomicLabs/GPT-S-1.4M with Docker Model Runner:
docker model run hf.co/AxiomicLabs/GPT-S-1.4M
| { | |
| "architectures": [ | |
| "GPTS14MForCausalLM" | |
| ], | |
| "attention_type": "grouped_query", | |
| "auto_map": { | |
| "AutoConfig": "configuration_gpts3.GPTS14MConfig", | |
| "AutoModelForCausalLM": "modeling_gpts3.GPTS14MForCausalLM" | |
| }, | |
| "bias": false, | |
| "dtype": "bfloat16", | |
| "embedding_scale": false, | |
| "head_dim": 32, | |
| "hidden_act": "silu", | |
| "hidden_size": 128, | |
| "intermediate_size": 341, | |
| "max_position_embeddings": 384, | |
| "model_type": "gpts14m", | |
| "num_attention_heads": 4, | |
| "num_hidden_layers": 5, | |
| "num_key_value_heads": 2, | |
| "rms_norm_eps": 1e-06, | |
| "rope_theta": 2500.0, | |
| "tie_word_embeddings": true, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "5.5.3", | |
| "vocab_size": 4096, | |
| "xsa_projection": true | |
| } | |