

# 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 :)

---
{"license": "apache-2.0", "datasets": ["VMware/open-instruct"], "base_model": "BEE-spoke-data/smol_llama-220M-GQA", "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.  \n   \n### Instruction:  \n  \nWrite an ode to Chipotle burritos. \n  \n### Response:  \n", "example_title": "burritos"}]}
transformers safetensors llama text-generation dataset:VMware/open-instruct base_model:BEE-spoke-data/smol_llama-220M-GQA license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us