Text Generation
Transformers
Safetensors
PyTorch
English
axiom
causal-lm
fine-tuned
instruct-model
custom-architecture
tiktoken
chatml
custom_code
Instructions to use user-anto/Axiom-Dense-380M-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use user-anto/Axiom-Dense-380M-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="user-anto/Axiom-Dense-380M-Instruct", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("user-anto/Axiom-Dense-380M-Instruct", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use user-anto/Axiom-Dense-380M-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "user-anto/Axiom-Dense-380M-Instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "user-anto/Axiom-Dense-380M-Instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/user-anto/Axiom-Dense-380M-Instruct
- SGLang
How to use user-anto/Axiom-Dense-380M-Instruct 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 "user-anto/Axiom-Dense-380M-Instruct" \ --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": "user-anto/Axiom-Dense-380M-Instruct", "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 "user-anto/Axiom-Dense-380M-Instruct" \ --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": "user-anto/Axiom-Dense-380M-Instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use user-anto/Axiom-Dense-380M-Instruct with Docker Model Runner:
docker model run hf.co/user-anto/Axiom-Dense-380M-Instruct
Download export_manifest.json from user-anto/Axiom-Dense-380M-Instruct: direct link, hf CLI and curl.
- Browser
- Download file 163 Bytes
-
https://huggingface.co/user-anto/Axiom-Dense-380M-Instruct/resolve/main/export_manifest.json
- Command line
-
hf download hf://user-anto/Axiom-Dense-380M-Instruct/export_manifest.json
-
curl -L -o export_manifest.json https://huggingface.co/user-anto/Axiom-Dense-380M-Instruct/resolve/main/export_manifest.json
163 Bytes
| { | |
| "source_checkpoint": "sft_checkpoints/step_0000641.pt", | |
| "step": 641, | |
| "has_optimizer_state": true, | |
| "export_time_utc": "2026-05-29T21:27:20.712310+00:00" | |
| } |