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---
base_model: BEE-spoke-data/smol_llama-220M-open_instruct
datasets:
- VMware/open-instruct
inference: false
license: apache-2.0
model_creator: BEE-spoke-data
model_name: smol_llama-220M-open_instruct
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-open_instruct-GGUF
Quantized GGUF model files for [smol_llama-220M-open_instruct](https://huggingface.co/BEE-spoke-data/smol_llama-220M-open_instruct) from [BEE-spoke-data](https://huggingface.co/BEE-spoke-data)
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [smol_llama-220m-open_instruct.fp16.gguf](https://huggingface.co/afrideva/smol_llama-220M-open_instruct-GGUF/resolve/main/smol_llama-220m-open_instruct.fp16.gguf) | fp16 | 436.50 MB |
| [smol_llama-220m-open_instruct.q2_k.gguf](https://huggingface.co/afrideva/smol_llama-220M-open_instruct-GGUF/resolve/main/smol_llama-220m-open_instruct.q2_k.gguf) | q2_k | 94.43 MB |
| [smol_llama-220m-open_instruct.q3_k_m.gguf](https://huggingface.co/afrideva/smol_llama-220M-open_instruct-GGUF/resolve/main/smol_llama-220m-open_instruct.q3_k_m.gguf) | q3_k_m | 114.65 MB |
| [smol_llama-220m-open_instruct.q4_k_m.gguf](https://huggingface.co/afrideva/smol_llama-220M-open_instruct-GGUF/resolve/main/smol_llama-220m-open_instruct.q4_k_m.gguf) | q4_k_m | 137.58 MB |
| [smol_llama-220m-open_instruct.q5_k_m.gguf](https://huggingface.co/afrideva/smol_llama-220M-open_instruct-GGUF/resolve/main/smol_llama-220m-open_instruct.q5_k_m.gguf) | q5_k_m | 157.91 MB |
| [smol_llama-220m-open_instruct.q6_k.gguf](https://huggingface.co/afrideva/smol_llama-220M-open_instruct-GGUF/resolve/main/smol_llama-220m-open_instruct.q6_k.gguf) | q6_k | 179.52 MB |
| [smol_llama-220m-open_instruct.q8_0.gguf](https://huggingface.co/afrideva/smol_llama-220M-open_instruct-GGUF/resolve/main/smol_llama-220m-open_instruct.q8_0.gguf) | q8_0 | 232.28 MB |
## Original Model Card:
# 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.
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 :)
--- |