---
base_model: BEE-spoke-data/smol_llama-220M-openhermes
datasets:
- teknium/openhermes
inference: false
license: apache-2.0
model_creator: BEE-spoke-data
model_name: smol_llama-220M-openhermes
pipeline_tag: text-generation
quantized_by: afrideva
tags:
- gguf
- ggml
- quantized
- q2_k
- q3_k_m
- q4_k_m
- q5_k_m
- q6_k
- q8_0
widget:
- example_title: burritos
  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"
---
# BEE-spoke-data/smol_llama-220M-openhermes-GGUF

Quantized GGUF model files for [smol_llama-220M-openhermes](https://huggingface.co/BEE-spoke-data/smol_llama-220M-openhermes) from [BEE-spoke-data](https://huggingface.co/BEE-spoke-data)


| Name | Quant method | Size |
| ---- | ---- | ---- |
| [smol_llama-220m-openhermes.fp16.gguf](https://huggingface.co/afrideva/smol_llama-220M-openhermes-GGUF/resolve/main/smol_llama-220m-openhermes.fp16.gguf) | fp16 | 436.50 MB  |
| [smol_llama-220m-openhermes.q2_k.gguf](https://huggingface.co/afrideva/smol_llama-220M-openhermes-GGUF/resolve/main/smol_llama-220m-openhermes.q2_k.gguf) | q2_k | 94.43 MB  |
| [smol_llama-220m-openhermes.q3_k_m.gguf](https://huggingface.co/afrideva/smol_llama-220M-openhermes-GGUF/resolve/main/smol_llama-220m-openhermes.q3_k_m.gguf) | q3_k_m | 114.65 MB  |
| [smol_llama-220m-openhermes.q4_k_m.gguf](https://huggingface.co/afrideva/smol_llama-220M-openhermes-GGUF/resolve/main/smol_llama-220m-openhermes.q4_k_m.gguf) | q4_k_m | 137.58 MB  |
| [smol_llama-220m-openhermes.q5_k_m.gguf](https://huggingface.co/afrideva/smol_llama-220M-openhermes-GGUF/resolve/main/smol_llama-220m-openhermes.q5_k_m.gguf) | q5_k_m | 157.91 MB  |
| [smol_llama-220m-openhermes.q6_k.gguf](https://huggingface.co/afrideva/smol_llama-220M-openhermes-GGUF/resolve/main/smol_llama-220m-openhermes.q6_k.gguf) | q6_k | 179.52 MB  |
| [smol_llama-220m-openhermes.q8_0.gguf](https://huggingface.co/afrideva/smol_llama-220M-openhermes-GGUF/resolve/main/smol_llama-220m-openhermes.q8_0.gguf) | q8_0 | 232.28 MB  |



## Original Model Card:
# 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.


![image/png](https://cdn-uploads.huggingface.co/production/uploads/60bccec062080d33f875cd0c/0nFP2jsBkritnryKmI8NV.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: 1.03
    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  


---