|
--- |
|
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. |
|
|
|
|
|
 |
|
|
|
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 |
|
|
|
|
|
--- |
