Text Generation
Transformers
Safetensors
PyTorch
English
gpt2
hardware-bus
memory-augmented
toolformer
slm
autonomous-agent
ssd-memory
edge-ai
text-generation-inference
Instructions to use AvinashRicky/AViGPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AvinashRicky/AViGPT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AvinashRicky/AViGPT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AvinashRicky/AViGPT") model = AutoModelForCausalLM.from_pretrained("AvinashRicky/AViGPT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AvinashRicky/AViGPT with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AvinashRicky/AViGPT" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AvinashRicky/AViGPT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AvinashRicky/AViGPT
- SGLang
How to use AvinashRicky/AViGPT 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 "AvinashRicky/AViGPT" \ --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": "AvinashRicky/AViGPT", "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 "AvinashRicky/AViGPT" \ --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": "AvinashRicky/AViGPT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AvinashRicky/AViGPT with Docker Model Runner:
docker model run hf.co/AvinashRicky/AViGPT
Download tokenizer_config.json from AvinashRicky/AViGPT: direct link, hf CLI and curl.
- Browser
- Download file 557 Bytes
-
https://huggingface.co/AvinashRicky/AViGPT/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://AvinashRicky/AViGPT/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/AvinashRicky/AViGPT/resolve/main/tokenizer_config.json
557 Bytes
| { | |
| "add_prefix_space": false, | |
| "backend": "tokenizers", | |
| "bos_token": "<|endoftext|>", | |
| "eos_token": "<|endoftext|>", | |
| "errors": "replace", | |
| "extra_special_tokens": [ | |
| "<|intent_start|>", | |
| "<|intent_end|>", | |
| "<|mem_query|>", | |
| "<|mem_query_end|>", | |
| "<|mem_payload|>", | |
| "<|mem_payload_end|>", | |
| "<|calc|>", | |
| "<|calc_end|>", | |
| "<|synthesize|>" | |
| ], | |
| "is_local": true, | |
| "local_files_only": false, | |
| "model_max_length": 1024, | |
| "pad_token": "<|endoftext|>", | |
| "tokenizer_class": "GPT2Tokenizer", | |
| "unk_token": "<|endoftext|>" | |
| } | |