Text Generation
Transformers
Safetensors
text generation
Deci AI
DeciCoder
custom_code
Eval Results (legacy)
Instructions to use Deci/DeciCoder-1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Deci/DeciCoder-1b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Deci/DeciCoder-1b", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Deci/DeciCoder-1b", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Deci/DeciCoder-1b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Deci/DeciCoder-1b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Deci/DeciCoder-1b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Deci/DeciCoder-1b
- SGLang
How to use Deci/DeciCoder-1b 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 "Deci/DeciCoder-1b" \ --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": "Deci/DeciCoder-1b", "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 "Deci/DeciCoder-1b" \ --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": "Deci/DeciCoder-1b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Deci/DeciCoder-1b with Docker Model Runner:
docker model run hf.co/Deci/DeciCoder-1b
add padding_mask
Browse files- modeling_decicoder.py +1 -0
modeling_decicoder.py
CHANGED
|
@@ -61,6 +61,7 @@ class DeciCoderAttention(LlamaAttention):
|
|
| 61 |
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
| 62 |
output_attentions: bool = False,
|
| 63 |
use_cache: bool = False,
|
|
|
|
| 64 |
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
| 65 |
bsz, q_len, _ = hidden_states.size()
|
| 66 |
if past_key_value is None:
|
|
|
|
| 61 |
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
| 62 |
output_attentions: bool = False,
|
| 63 |
use_cache: bool = False,
|
| 64 |
+
padding_mask: Optional[torch.LongTensor] = None,
|
| 65 |
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
| 66 |
bsz, q_len, _ = hidden_states.size()
|
| 67 |
if past_key_value is None:
|