---
license: apache-2.0
base_model: BEE-spoke-data/smol_llama-220M-GQA
datasets:
- VMware/open-instruct
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: 1.04
    no_repeat_ngram_size: 6
    epsilon_cutoff: 0.0006
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-open_instruct

> 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.  

### Response:
```

This was **not** trained using a separate 'inputs' field (as `VMware/open-instruct` doesn't use one).


## 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.


![image/png](https://cdn-uploads.huggingface.co/production/uploads/60bccec062080d33f875cd0c/MdOB7TD5UosPGZvdZWG0I.png)

Note that the inference API parameters used here are an initial educated guess, and may be updated over time:

```yml
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: 1.04
    no_repeat_ngram_size: 6
    epsilon_cutoff: 0.0006
```

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 

This was trained on `VMware/open-instruct` so do whatever you want, provided it falls under the base apache-2.0 license :)

---