Text Generation
Transformers
PyTorch
Safetensors
English
llama
legal
Eval Results (legacy)
text-generation-inference
Instructions to use AdaptLLM/law-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AdaptLLM/law-chat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AdaptLLM/law-chat")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/law-chat") model = AutoModelForCausalLM.from_pretrained("AdaptLLM/law-chat", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AdaptLLM/law-chat with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AdaptLLM/law-chat" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AdaptLLM/law-chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AdaptLLM/law-chat
- SGLang
How to use AdaptLLM/law-chat 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/law-chat" \ --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/law-chat", "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/law-chat" \ --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/law-chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AdaptLLM/law-chat with Docker Model Runner:
docker model run hf.co/AdaptLLM/law-chat
Update README.md
Browse files
README.md
CHANGED
|
@@ -1,14 +1,24 @@
|
|
| 1 |
---
|
| 2 |
language:
|
| 3 |
- en
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 4 |
---
|
| 5 |
|
| 6 |
-
# Adapt Large Language Models to Domains
|
| 7 |
This repo contains the domain-specific chat model developed from LLaMA-2-Chat-7B, using the method in our paper [Adapting Large Language Models via Reading Comprehension](https://huggingface.co/papers/2309.09530).
|
| 8 |
|
| 9 |
We explore **continued pre-training on domain-specific corpora** for large language models. While this approach enriches LLMs with domain knowledge, it significantly hurts their prompting ability for question answering. Inspired by human learning via reading comprehension, we propose a simple method to **transform large-scale pre-training corpora into reading comprehension texts**, consistently improving prompting performance across tasks in biomedicine, finance, and law domains. **Our 7B model competes with much larger domain-specific models like BloombergGPT-50B**.
|
| 10 |
|
| 11 |
-
### 🤗 We are currently working hard on developing models across different domains, scales and architectures!
|
| 12 |
|
| 13 |
**************************** **Updates** ****************************
|
| 14 |
* 12/8: Released our [chat models](https://huggingface.co/AdaptLLM/finance-chat) developed from LLaMA-2-Chat-7B.
|
|
@@ -68,4 +78,4 @@ If you find our work helpful, please cite us:
|
|
| 68 |
volume = {abs/2309.09530},
|
| 69 |
year = {2023}
|
| 70 |
}
|
| 71 |
-
```
|
|
|
|
| 1 |
---
|
| 2 |
language:
|
| 3 |
- en
|
| 4 |
+
license: llama2
|
| 5 |
+
datasets:
|
| 6 |
+
- EleutherAI/pile
|
| 7 |
+
- Open-Orca/OpenOrca
|
| 8 |
+
- GAIR/lima
|
| 9 |
+
- WizardLM/WizardLM_evol_instruct_V2_196k
|
| 10 |
+
metrics:
|
| 11 |
+
- accuracy
|
| 12 |
+
library_name: fairseq
|
| 13 |
+
pipeline_tag: text-generation
|
| 14 |
---
|
| 15 |
|
| 16 |
+
# Adapt (Large) Language Models to Domains
|
| 17 |
This repo contains the domain-specific chat model developed from LLaMA-2-Chat-7B, using the method in our paper [Adapting Large Language Models via Reading Comprehension](https://huggingface.co/papers/2309.09530).
|
| 18 |
|
| 19 |
We explore **continued pre-training on domain-specific corpora** for large language models. While this approach enriches LLMs with domain knowledge, it significantly hurts their prompting ability for question answering. Inspired by human learning via reading comprehension, we propose a simple method to **transform large-scale pre-training corpora into reading comprehension texts**, consistently improving prompting performance across tasks in biomedicine, finance, and law domains. **Our 7B model competes with much larger domain-specific models like BloombergGPT-50B**.
|
| 20 |
|
| 21 |
+
### 🤗 We are currently working hard on developing models across different domains, scales and architectures! Please stay tuned! 🤗
|
| 22 |
|
| 23 |
**************************** **Updates** ****************************
|
| 24 |
* 12/8: Released our [chat models](https://huggingface.co/AdaptLLM/finance-chat) developed from LLaMA-2-Chat-7B.
|
|
|
|
| 78 |
volume = {abs/2309.09530},
|
| 79 |
year = {2023}
|
| 80 |
}
|
| 81 |
+
```
|