Instructions to use Deci/DeciLM-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Deci/DeciLM-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Deci/DeciLM-7B", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Deci/DeciLM-7B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Deci/DeciLM-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Deci/DeciLM-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Deci/DeciLM-7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Deci/DeciLM-7B
- SGLang
How to use Deci/DeciLM-7B 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-7B" \ --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": "Deci/DeciLM-7B", "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 "Deci/DeciLM-7B" \ --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": "Deci/DeciLM-7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Deci/DeciLM-7B with Docker Model Runner:
docker model run hf.co/Deci/DeciLM-7B
Update README.md
Browse files
README.md
CHANGED
|
@@ -15,7 +15,7 @@ DeciLM-7B is a 7.04 billion parameter decoder-only text generation model, releas
|
|
| 15 |
|
| 16 |
Deci developed and released the DeciLM-7B language model, a pre-trained, high-efficiency text generation model with 7 billion parameters. DeciLM-7B is not only the most accurate 7B base model, but it also outpaces all models in its class with a throughput that is up to 4.4x that of Mistral-7B's. An instruct version [DeciLM-7B-instruct](https://huggingface.co/Deci/DeciLM-7B-instruct) has also been released.
|
| 17 |
|
| 18 |
-
- **Developed by:** [Deci](https://deci.ai/)
|
| 19 |
- **Model type:** DeciLM is an auto-regressive language model using an optimized transformer decoder architecture that includes variable Grouped-Query Attention.
|
| 20 |
- **Language(s) (NLP):** English
|
| 21 |
- **License:** Apache 2.0
|
|
@@ -31,7 +31,7 @@ Deci developed and released the DeciLM-7B language model, a pre-trained, high-ef
|
|
| 31 |
|
| 32 |
### Model Sources
|
| 33 |
|
| 34 |
-
- **Blog:** [DeciLM-7B Technical Blog](https://deci.ai/blog/introducing-DeciLM-7B-the-fastest-and-most-accurate-7b-large-language-model-to-date)
|
| 35 |
- **Demo:** [DeciLM-7B-instruct Demo](https://huggingface.co/spaces/Deci/DeciLM-7B-instruct)
|
| 36 |
- **Finetuning Notebook:** [DeciLM-7B Finetuning Notebook](https://colab.research.google.com/drive/1kEV6i96AQ94xTCvSd11TxkEaksTb5o3U?usp=sharing)
|
| 37 |
- **Text Generation Notebook:** [DeciLM-7B-instruct Text Generation Notebook](https://bit.ly/declm-7b-instruct)
|
|
|
|
| 15 |
|
| 16 |
Deci developed and released the DeciLM-7B language model, a pre-trained, high-efficiency text generation model with 7 billion parameters. DeciLM-7B is not only the most accurate 7B base model, but it also outpaces all models in its class with a throughput that is up to 4.4x that of Mistral-7B's. An instruct version [DeciLM-7B-instruct](https://huggingface.co/Deci/DeciLM-7B-instruct) has also been released.
|
| 17 |
|
| 18 |
+
- **Developed by:** [Deci](https://deci.ai/?utm_campaign=repos&utm_source=hugging-face&utm_medium=model-card&utm_content=decilm-7b)
|
| 19 |
- **Model type:** DeciLM is an auto-regressive language model using an optimized transformer decoder architecture that includes variable Grouped-Query Attention.
|
| 20 |
- **Language(s) (NLP):** English
|
| 21 |
- **License:** Apache 2.0
|
|
|
|
| 31 |
|
| 32 |
### Model Sources
|
| 33 |
|
| 34 |
+
- **Blog:** [DeciLM-7B Technical Blog](https://deci.ai/blog/introducing-DeciLM-7B-the-fastest-and-most-accurate-7b-large-language-model-to-date/?utm_campaign=repos&utm_source=hugging-face&utm_medium=model-card&utm_content=decilm-7b)
|
| 35 |
- **Demo:** [DeciLM-7B-instruct Demo](https://huggingface.co/spaces/Deci/DeciLM-7B-instruct)
|
| 36 |
- **Finetuning Notebook:** [DeciLM-7B Finetuning Notebook](https://colab.research.google.com/drive/1kEV6i96AQ94xTCvSd11TxkEaksTb5o3U?usp=sharing)
|
| 37 |
- **Text Generation Notebook:** [DeciLM-7B-instruct Text Generation Notebook](https://bit.ly/declm-7b-instruct)
|