Instructions to use hf-internal-testing/tiny-random-GPTBigCodeForCausalLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-GPTBigCodeForCausalLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="hf-internal-testing/tiny-random-GPTBigCodeForCausalLM")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPTBigCodeForCausalLM") model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-GPTBigCodeForCausalLM") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use hf-internal-testing/tiny-random-GPTBigCodeForCausalLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "hf-internal-testing/tiny-random-GPTBigCodeForCausalLM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hf-internal-testing/tiny-random-GPTBigCodeForCausalLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/hf-internal-testing/tiny-random-GPTBigCodeForCausalLM
- SGLang
How to use hf-internal-testing/tiny-random-GPTBigCodeForCausalLM 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 "hf-internal-testing/tiny-random-GPTBigCodeForCausalLM" \ --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": "hf-internal-testing/tiny-random-GPTBigCodeForCausalLM", "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 "hf-internal-testing/tiny-random-GPTBigCodeForCausalLM" \ --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": "hf-internal-testing/tiny-random-GPTBigCodeForCausalLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use hf-internal-testing/tiny-random-GPTBigCodeForCausalLM with Docker Model Runner:
docker model run hf.co/hf-internal-testing/tiny-random-GPTBigCodeForCausalLM
| { | |
| "activation_function": "relu", | |
| "architectures": [ | |
| "GPTBigCodeForCausalLM" | |
| ], | |
| "attention_softmax_in_fp32": false, | |
| "attn_pdrop": 0.1, | |
| "bos_token_id": 0, | |
| "embd_pdrop": 0.1, | |
| "eos_token_id": 0, | |
| "gradient_checkpointing": false, | |
| "initializer_range": 0.02, | |
| "is_decoder": true, | |
| "layer_norm_epsilon": 1e-05, | |
| "model_type": "gpt_bigcode", | |
| "multi_query": true, | |
| "n_embd": 32, | |
| "n_head": 4, | |
| "n_inner": 37, | |
| "n_layer": 5, | |
| "n_positions": 512, | |
| "pad_token_id": 1021, | |
| "reorder_and_upcast_attn": false, | |
| "resid_pdrop": 0.1, | |
| "scale_attention_softmax_in_fp32": false, | |
| "scale_attn_by_inverse_layer_idx": false, | |
| "scale_attn_weights": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.28.0.dev0", | |
| "type_vocab_size": 16, | |
| "use_cache": true, | |
| "vocab_size": 1024 | |
| } | |