agentlans/pythia-70m-lmsys-prompts

Pythia 70M LMSYS Prompt Generator

This model generates user prompts based on the lmsys/lmsys-chat-1m dataset. Since the original dataset is restricted, this model provides accessible prompt generation derived from it. It is a fine-tuned version of EleutherAI/pythia-70m-deduped.

Evaluation results on the validation set are:

• —Loss: 2.6662
• —Accuracy: 0.5068

Example usage

from transformers import pipeline, set_seed

generator = pipeline('text-generation', model='agentlans/pythia-70m-lmsys-prompts', device='cuda')

set_seed(20250906) # For reproducibility
# Generate starting from empty string
results = generator("", max_length=3000, num_return_sequences=5, do_sample=True)

for i, x in enumerate(results, 1):
    print(f"**Prompt {i}:**\n\n```\n{x['generated_text']}\n```\n")

Sample output:

Prompt 1:

Which are the number of 10 cars to buy for 20 cars for a 3,000 person in 20 years?
Answer Choices: (A) the best car in the world. (B) The reason why... [truncated for brevity]

Prompt 2:

can you tell me which version is better to serve as a chatgpt manager.

Prompt 3:

write a story using the following NAME_1 game, choose the theme, do a story... [truncated for brevity]

Prompt 4:

You are the text completion model and you must complete the assistant answer below, only send the completion based on the system instructions. Don't repeat your answer sentences.
user: descriptive answer for python how can I import yurt to another language in python?
assistant:

Prompt 5:

write a story with 10 paragraphs describing how a person is reading a book called "NAME_1".

Limitations

• —Generated prompts may be incoherent or nonsensical.
• —The underlying EleutherAI Pythia model has limited capability with code and non-English text.
• —Some outputs may reflect offensive or inappropriate content present in the original dataset.
• —Name placeholders like NAME_1 are used and may appear untranslated or unpopulated.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 5e-05
• —trainbatchsize: 8
• —evalbatchsize: 8
• —seed: 42
• —optimizer: Use adamwtorch with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
• —lrschedulertype: linear
• —num_epochs: 5.0

Training results

Framework versions

• —Transformers 4.51.3
• —Pytorch 2.6.0+cu124
• —Datasets 3.2.0
• —Tokenizers 0.21.0