Instructions to use Deci/DeciCoder-6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Deci/DeciCoder-6B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Deci/DeciCoder-6B", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Deci/DeciCoder-6B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Deci/DeciCoder-6B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Deci/DeciCoder-6B" # 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-6B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Deci/DeciCoder-6B
- SGLang
How to use Deci/DeciCoder-6B 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-6B" \ --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-6B", "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-6B" \ --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-6B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Deci/DeciCoder-6B with Docker Model Runner:
docker model run hf.co/Deci/DeciCoder-6B
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pipeline_tag: text-generation
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license: apache-2.0
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tags:
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## Model Architecture
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| Parameters | Layers | Heads | Sequence Length | GQA num_key_value_heads |
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| 6B | 32 | 32 | 4096 | Variable |
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- **Decoder layer:** Variable Grouped Query Attention
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- **Position Embeddings:** Rotary Position Embeddings [Su et al., 2021](https://arxiv.org/abs/2104.09864)
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## Uses
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The model is intended to perform single/multiline code completion from a
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context window of up to 4096k tokens. It is *not* an instruction model
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and commands like \"Write a function that computes the absolute value of
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an integer,\" won't yield the desired results. A more effective approach
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is to frame instructions in the style of source code comments (e.g. \#
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this function calculates the absolute value of an integer) or to present
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a function signature and docstring, enabling the model to complete the
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function's body.
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### How to Use
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pipeline_tag: text-generation
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license: apache-2.0
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tags:
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## Model Architecture
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| Parameters | Layers | Heads | Sequence Length | GQA num_key_value_heads |
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| 6B | 32 | 32 | 4096 | Variable |
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- **Decoder layer:** Variable Grouped Query Attention
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- **Position Embeddings:** Rotary Position Embeddings [Su et al., 2021](https://arxiv.org/abs/2104.09864)
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### How to Use
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