license: apache-2.0
base_model: BEE-spoke-data/smol_llama-220M-GQA
datasets:
- teknium/openhermes
inference:
parameters:
do_sample: true
renormalize_logits: true
temperature: 0.25
top_p: 0.95
top_k: 50
min_new_tokens: 2
max_new_tokens: 96
repetition_penalty: 1.03
no_repeat_ngram_size: 5
epsilon_cutoff: 0.0008
widget:
- text: >
Below is an instruction that describes a task, paired with an input that
provides further context. Write a response that appropriately completes
the request.
### Instruction:
Write an ode to Chipotle burritos.
### Response:
example_title: burritos
BEE-spoke-data/smol_llama-220M-openhermes
Please note that this is an experiment, and the model has limitations because it is smol.
prompt format is alpaca
Below is an instruction that describes a task, paired with an input that
provides further context. Write a response that appropriately completes
the request.
### Instruction:
How can I increase my meme production/output? Currently, I only create them in ancient babylonian which is time consuming.
### Inputs:
### Response:
It was trained on inputs so if you have inputs (like some text to ask a question about) then include it under ### Inputs:
Example
Output on the text above ^. The inference API is set to sample with low temp so you should see (at least slightly) different generations each time.
Note that the inference API parameters used here are an initial educated guess, and may be updated over time:
inference:
parameters:
do_sample: true
renormalize_logits: true
temperature: 0.25
top_p: 0.95
top_k: 50
min_new_tokens: 2
max_new_tokens: 96
repetition_penalty: 1.03
no_repeat_ngram_size: 5
epsilon_cutoff: 0.0008
Feel free to experiment with the parameters using the model in Python and let us know if you have improved results with other params!
Data
Note that this checkpoint was fine-tuned on teknium/openhermes, which is generated/synthetic data by an OpenAI model. This means usage of this checkpoint should follow their terms of use: https://openai.com/policies/terms-of-use