Text Generation
Transformers
Safetensors
English
Deci AI
DeciLM
Instruction
custom_code
Eval Results (legacy)
Instructions to use Deci/DeciLM-6b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Deci/DeciLM-6b-instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Deci/DeciLM-6b-instruct", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Deci/DeciLM-6b-instruct", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Deci/DeciLM-6b-instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Deci/DeciLM-6b-instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Deci/DeciLM-6b-instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Deci/DeciLM-6b-instruct
- SGLang
How to use Deci/DeciLM-6b-instruct 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/DeciLM-6b-instruct" \ --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/DeciLM-6b-instruct", "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/DeciLM-6b-instruct" \ --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/DeciLM-6b-instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Deci/DeciLM-6b-instruct with Docker Model Runner:
docker model run hf.co/Deci/DeciLM-6b-instruct
Update hf_benchmark_example.py
Browse files- hf_benchmark_example.py +2 -2
hf_benchmark_example.py
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You need a file called "sample.txt" (default path) with text to take tokens for prompts or supply --text_file "path/to/text.txt" as an argument to a text file.
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You can use our attached "sample.txt" file with one of Deci's blogs as a prompt.
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# Run this and record tokens per second (652 tokens per second on A10 for DeciLM-6b)
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python
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# Run this and record tokens per second (136 tokens per second on A10 for meta-llama/Llama-2-7b-hf), CUDA OOM above batch size 8
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python
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"""
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import json
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You need a file called "sample.txt" (default path) with text to take tokens for prompts or supply --text_file "path/to/text.txt" as an argument to a text file.
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You can use our attached "sample.txt" file with one of Deci's blogs as a prompt.
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# Run this and record tokens per second (652 tokens per second on A10 for DeciLM-6b)
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python hf_benchmark_example.py --model Deci/DeciLM-6b-instruct
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# Run this and record tokens per second (136 tokens per second on A10 for meta-llama/Llama-2-7b-hf), CUDA OOM above batch size 8
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python hf_benchmark_example.py --model meta-llama/Llama-2-7b-hf --batch_size 8
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"""
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import json
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