Instructions to use cesun/cbllm-generation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cesun/cbllm-generation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="cesun/cbllm-generation")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("cesun/cbllm-generation", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use cesun/cbllm-generation with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cesun/cbllm-generation" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cesun/cbllm-generation", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/cesun/cbllm-generation
- SGLang
How to use cesun/cbllm-generation 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 "cesun/cbllm-generation" \ --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": "cesun/cbllm-generation", "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 "cesun/cbllm-generation" \ --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": "cesun/cbllm-generation", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use cesun/cbllm-generation with Docker Model Runner:
docker model run hf.co/cesun/cbllm-generation
Add model card with metadata for CB-LLM
#1
by nielsr HF Staff - opened
README.md
ADDED
|
@@ -0,0 +1,35 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
pipeline_tag: text-generation
|
| 3 |
+
library_name: transformers
|
| 4 |
+
license: apache-2.0
|
| 5 |
+
tags:
|
| 6 |
+
- interpretable
|
| 7 |
+
- text-classification
|
| 8 |
+
- text-generation
|
| 9 |
+
---
|
| 10 |
+
|
| 11 |
+
# Concept Bottleneck Large Language Models
|
| 12 |
+
|
| 13 |
+
This repository contains the Concept Bottleneck Large Language Model (CB-LLM) presented in [Concept Bottleneck Large Language Models](https://huggingface.co/papers/2412.07992).
|
| 14 |
+
|
| 15 |
+
[Project Website](https://lilywenglab.github.io/CB-LLMs/)
|
| 16 |
+
|
| 17 |
+
Code: [https://github.com/Trustworthy-ML-Lab/CB-LLMs](https://github.com/Trustworthy-ML-Lab/CB-LLMs)
|
| 18 |
+
|
| 19 |
+
This model offers inherent interpretability and controllability in text generation. See the linked paper and GitHub repository for details on training and usage.
|
| 20 |
+
|
| 21 |
+
## Usage (Example - Text Generation)
|
| 22 |
+
|
| 23 |
+
```python
|
| 24 |
+
from transformers import pipeline, AutoTokenizer
|
| 25 |
+
|
| 26 |
+
model_name = "cesun/cbllm-generation" #replace with actual model name
|
| 27 |
+
pipe = pipeline(
|
| 28 |
+
"text-generation",
|
| 29 |
+
model_name,
|
| 30 |
+
tokenizer=AutoTokenizer.from_pretrained(model_name, trust_remote_code=True),
|
| 31 |
+
device_map="auto",
|
| 32 |
+
trust_remote_code=True,
|
| 33 |
+
)
|
| 34 |
+
print(pipe("The key to life is", max_new_tokens=20, do_sample=True)[0]["generated_text"])
|
| 35 |
+
```
|