Instructions to use deepcode-ai/coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepcode-ai/coder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="deepcode-ai/coder") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("deepcode-ai/coder") model = AutoModelForCausalLM.from_pretrained("deepcode-ai/coder") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use deepcode-ai/coder with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "deepcode-ai/coder" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "deepcode-ai/coder", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/deepcode-ai/coder
- SGLang
How to use deepcode-ai/coder 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 "deepcode-ai/coder" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "deepcode-ai/coder", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "deepcode-ai/coder" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "deepcode-ai/coder", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use deepcode-ai/coder with Docker Model Runner:
docker model run hf.co/deepcode-ai/coder
| { | |
| "_name_or_path": "/checkpoint/dpf/models/cm-1.3B-hf", | |
| "activation_dropout": 0.0, | |
| "activation_function": "gelu", | |
| "architectures": [ | |
| "XGLMForCausalLM" | |
| ], | |
| "attention_dropout": 0.1, | |
| "attention_heads": 32, | |
| "bos_token_id": 0, | |
| "d_model": 2048, | |
| "decoder_start_token_id": 2, | |
| "dropout": 0.1, | |
| "eos_token_id": 2, | |
| "ffn_dim": 8192, | |
| "init_std": 0.02, | |
| "layerdrop": 0.0, | |
| "max_position_embeddings": 2048, | |
| "model_type": "xglm", | |
| "num_layers": 24, | |
| "pad_token_id": 1, | |
| "scale_embedding": true, | |
| "torch_dtype": "float16", | |
| "transformers_version": "4.18.0.dev0", | |
| "use_cache": true, | |
| "vocab_size": 50518 | |
| } | |