wxkkxw/microsoft_DialoGPT-small_databricks-dolly-15k_qlora
0
microsoftDialoGPT-smalldatabricks-dolly-15k_qlora v1.0
Model Description
Hello
Base Model: microsoft/DialoGPT-small
Developed by: Mathhead
Training Details
Dataset
- Training Data: databricks/databricks-dolly-15k
- Dataset Size: {dataset_size}
- Training Duration: {training_duration}
- Hardware: {hardware}
Hyperparameters
- num_epochs: 1
- batch_size: 1
- eval_batch_size: 4
- gradient_accumulation_steps: 4
- optim: adamw_torch
- save_steps: 25
- logging_steps: 5
- learning_rate: 0.0002
- weight_decay: 0.001
- max_grad_norm: 0.3
- max_steps: 50
- warmup_ratio: 0.03
- report_to: ['tensorboard']
- eval_steps: 25
- save_total_limit: 3
Evaluation Metrics
- eval_loss: 5.95399284362793
- eval_runtime: 83.5159
- eval_samples_per_second: 1.197
- eval_steps_per_second: 0.299
- eval_entropy: 4.078821926116944
- eval_num_tokens: 21934.0
- eval_mean_token_accuracy: 0.2594447863101959
- epoch: 2.0
Test Dataset: databricks/databricks-dolly-15k
Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("wxkkxw/microsoft_DialoGPT-small_databricks-dolly-15k_qlora")
model = AutoModelForCausalLM.from_pretrained("wxkkxw/microsoft_DialoGPT-small_databricks-dolly-15k_qlora")
# Generate text
prompt = "{example_prompt}"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
**inputs,
max_new_tokens={max_new_tokens},
temperature={temperature},
top_p={top_p}
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)Limitations and Biases
{limitations_list}
Citation
@misc{{{citation_key},
author = {{{citation_authors}}},
title = {{{citation_title}}},
year = {{{citation_year}}},
publisher = {{Hugging Face}},
url = {{https://huggingface.co/wxkkxw/microsoft_DialoGPT-small_databricks-dolly-15k_qlora}}
}}Model Card Authors
Mathhead
Model Card Contact
For questions and feedback, please contact: {contact_email}
This model card was generated from template on 2025-11-15.
