Instructions to use AdaptLLM/finance-LLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AdaptLLM/finance-LLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AdaptLLM/finance-LLM")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/finance-LLM") model = AutoModelForCausalLM.from_pretrained("AdaptLLM/finance-LLM") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use AdaptLLM/finance-LLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AdaptLLM/finance-LLM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AdaptLLM/finance-LLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AdaptLLM/finance-LLM
- SGLang
How to use AdaptLLM/finance-LLM 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 "AdaptLLM/finance-LLM" \ --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": "AdaptLLM/finance-LLM", "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 "AdaptLLM/finance-LLM" \ --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": "AdaptLLM/finance-LLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AdaptLLM/finance-LLM with Docker Model Runner:
docker model run hf.co/AdaptLLM/finance-LLM
Update README.md
Browse files
README.md
CHANGED
|
@@ -20,6 +20,7 @@ We explore **continued pre-training on domain-specific corpora** for large langu
|
|
| 20 |
### π€ [2024/6/21] We release the 2nd version of AdaptLLM at [Instruction-Pretrain](https://huggingface.co/instruction-pretrain), effective for both general pre-training from scratch and domain-adaptive continual pre-training!!! π€
|
| 21 |
|
| 22 |
**************************** **Updates** ****************************
|
|
|
|
| 23 |
* 2024/6/21: ππ» Released the 2nd version of AdaptLLM at [Instruction-Pretrain](https://huggingface.co/instruction-pretrain) ππ»
|
| 24 |
* 2024/1/16: π Our [research paper](https://huggingface.co/papers/2309.09530) has been accepted by ICLR 2024!!!π
|
| 25 |
* 2023/12/19: Released our [13B base models](https://huggingface.co/AdaptLLM/law-LLM-13B) developed from LLaMA-1-13B.
|
|
|
|
| 20 |
### π€ [2024/6/21] We release the 2nd version of AdaptLLM at [Instruction-Pretrain](https://huggingface.co/instruction-pretrain), effective for both general pre-training from scratch and domain-adaptive continual pre-training!!! π€
|
| 21 |
|
| 22 |
**************************** **Updates** ****************************
|
| 23 |
+
* 2024/6/22: Released the [benchmarking code](https://github.com/microsoft/LMOps/tree/main/adaptllm).
|
| 24 |
* 2024/6/21: ππ» Released the 2nd version of AdaptLLM at [Instruction-Pretrain](https://huggingface.co/instruction-pretrain) ππ»
|
| 25 |
* 2024/1/16: π Our [research paper](https://huggingface.co/papers/2309.09530) has been accepted by ICLR 2024!!!π
|
| 26 |
* 2023/12/19: Released our [13B base models](https://huggingface.co/AdaptLLM/law-LLM-13B) developed from LLaMA-1-13B.
|