README.md

---
datasets:
  - allenai/WildChat-1M
  - allenai/WildChat-1M-Full
  - allenai/WildChat
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---


# Model Card for WildLlama-7b-user-assistant

## Model Description

The WildLlama-7b-user-assistant model is a chatbot derived from the [Llama-2 model by Meta](https://huggingface.co/meta-llama/Llama-2-7b-hf) that is licensed under the [Llama 2 License](https://ai.meta.com/resources/models-and-libraries/llama-downloads/), enhanced through fine-tuning on the [WildChat Dataset](https://huggingface.co/datasets/allenai/WildChat)'s user-ChatGPT interactions. WildLlama-7b-user-assistant is trained to predict **both user prompts and assistant responses**. Note that this model is worse at generating assistant responses than [WildLlama-7b-assistant-only](https://huggingface.co/models/allenai/WildLlama-7b-assistant-only), which is trained to only predict assistant responses. If you need the best assistant response quality, please use [WildLlama-7b-assistant-only](https://huggingface.co/allenai/WildLlama-7b-assistant-only).

- **Model type:** Language model
- **Language(s) (NLP):** multi-lingual
- **License:** [**AI2
  ImpACT License - Medium Risk Artifacts ("MR
  Agreement")**](https://allenai.org/licenses/impact-mr)
- **Parent Model:** https://huggingface.co/meta-llama/Llama-2-7b-hf
- **Paper:** https://arxiv.org/abs/2405.01470
- **Visualization Tool:** https://wildvisualizer.com
- **Visualization Paper:** https://arxiv.org/abs/2409.03753

# Bias, Risks, and Limitations

Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.

## Recommendations

We recommend that this model not be used for any high-impact or human-facing purposes as its biases and limitations need to be further explored.
We intend this to be a research artifact to advance AI's ability to better serve human needs.

# Citation

**BibTeX:**

```
@inproceedings{
  zhao2024wildchat,
  title={WildChat: 1M Chat{GPT} Interaction Logs in the Wild},
  author={Wenting Zhao and Xiang Ren and Jack Hessel and Claire Cardie and Yejin Choi and Yuntian Deng},
  booktitle={The Twelfth International Conference on Learning Representations},
  year={2024},
  url={https://openreview.net/forum?id=Bl8u7ZRlbM}
  }
```

```
@misc{deng2024wildvisopensourcevisualizer,
  title={WildVis: Open Source Visualizer for Million-Scale Chat Logs in the Wild}, 
  author={Yuntian Deng and Wenting Zhao and Jack Hessel and Xiang Ren and Claire Cardie and Yejin Choi},
  year={2024},
  eprint={2409.03753},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2409.03753}, 
}
```

# How to Get Started with the Model

Use the code below to get started with the model.
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

device = "cuda" if torch.cuda.is_available() else "cpu"

model_name = 'allenai/WildLlama-7b-user-assistant'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name).to(device)

# Notice the spaces!
# Format: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: abc</s> ASSISTANT: def</s>USER:
# To generate a user prompt in the first turn
prompt = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:"
model_inputs = tokenizer(prompt, return_tensors='pt', add_special_tokens=False).to(device)
output = model.generate(**model_inputs)

print("Output:\n" + 100 * '-')
print(tokenizer.decode(output[0], skip_special_tokens=True))

# To generate an assistant response
prompt = tokenizer.decode(output[0], skip_special_tokens=False) + ' ASSISTANT:'
model_inputs = tokenizer(prompt,  return_tensors='pt', add_special_tokens=False).to(device)
output = model.generate(**model_inputs)

print("Output:\n" + 100 * '-')
print(tokenizer.decode(output[0], skip_special_tokens=True))

# To generate a user prompt in follow-up turns
prompt = tokenizer.decode(output[0], skip_special_tokens=False) + 'USER:'
model_inputs = tokenizer(prompt,  return_tensors='pt', add_special_tokens=False).to(device)
output = model.generate(**model_inputs)

print("Output:\n" + 100 * '-')
print(tokenizer.decode(output[0], skip_special_tokens=True))
```