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Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
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smol_llama-220M-openhermes - GGUF
- Model creator: https://huggingface.co/BEE-spoke-data/
- Original model: https://huggingface.co/BEE-spoke-data/smol_llama-220M-openhermes/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [smol_llama-220M-openhermes.Q2_K.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-openhermes-gguf/blob/main/smol_llama-220M-openhermes.Q2_K.gguf) | Q2_K | 0.09GB |
| [smol_llama-220M-openhermes.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-openhermes-gguf/blob/main/smol_llama-220M-openhermes.IQ3_XS.gguf) | IQ3_XS | 0.1GB |
| [smol_llama-220M-openhermes.IQ3_S.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-openhermes-gguf/blob/main/smol_llama-220M-openhermes.IQ3_S.gguf) | IQ3_S | 0.1GB |
| [smol_llama-220M-openhermes.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-openhermes-gguf/blob/main/smol_llama-220M-openhermes.Q3_K_S.gguf) | Q3_K_S | 0.1GB |
| [smol_llama-220M-openhermes.IQ3_M.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-openhermes-gguf/blob/main/smol_llama-220M-openhermes.IQ3_M.gguf) | IQ3_M | 0.1GB |
| [smol_llama-220M-openhermes.Q3_K.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-openhermes-gguf/blob/main/smol_llama-220M-openhermes.Q3_K.gguf) | Q3_K | 0.11GB |
| [smol_llama-220M-openhermes.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-openhermes-gguf/blob/main/smol_llama-220M-openhermes.Q3_K_M.gguf) | Q3_K_M | 0.11GB |
| [smol_llama-220M-openhermes.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-openhermes-gguf/blob/main/smol_llama-220M-openhermes.Q3_K_L.gguf) | Q3_K_L | 0.11GB |
| [smol_llama-220M-openhermes.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-openhermes-gguf/blob/main/smol_llama-220M-openhermes.IQ4_XS.gguf) | IQ4_XS | 0.12GB |
| [smol_llama-220M-openhermes.Q4_0.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-openhermes-gguf/blob/main/smol_llama-220M-openhermes.Q4_0.gguf) | Q4_0 | 0.12GB |
| [smol_llama-220M-openhermes.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-openhermes-gguf/blob/main/smol_llama-220M-openhermes.IQ4_NL.gguf) | IQ4_NL | 0.12GB |
| [smol_llama-220M-openhermes.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-openhermes-gguf/blob/main/smol_llama-220M-openhermes.Q4_K_S.gguf) | Q4_K_S | 0.12GB |
| [smol_llama-220M-openhermes.Q4_K.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-openhermes-gguf/blob/main/smol_llama-220M-openhermes.Q4_K.gguf) | Q4_K | 0.13GB |
| [smol_llama-220M-openhermes.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-openhermes-gguf/blob/main/smol_llama-220M-openhermes.Q4_K_M.gguf) | Q4_K_M | 0.13GB |
| [smol_llama-220M-openhermes.Q4_1.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-openhermes-gguf/blob/main/smol_llama-220M-openhermes.Q4_1.gguf) | Q4_1 | 0.13GB |
| [smol_llama-220M-openhermes.Q5_0.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-openhermes-gguf/blob/main/smol_llama-220M-openhermes.Q5_0.gguf) | Q5_0 | 0.14GB |
| [smol_llama-220M-openhermes.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-openhermes-gguf/blob/main/smol_llama-220M-openhermes.Q5_K_S.gguf) | Q5_K_S | 0.14GB |
| [smol_llama-220M-openhermes.Q5_K.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-openhermes-gguf/blob/main/smol_llama-220M-openhermes.Q5_K.gguf) | Q5_K | 0.15GB |
| [smol_llama-220M-openhermes.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-openhermes-gguf/blob/main/smol_llama-220M-openhermes.Q5_K_M.gguf) | Q5_K_M | 0.15GB |
| [smol_llama-220M-openhermes.Q5_1.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-openhermes-gguf/blob/main/smol_llama-220M-openhermes.Q5_1.gguf) | Q5_1 | 0.16GB |
| [smol_llama-220M-openhermes.Q6_K.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-openhermes-gguf/blob/main/smol_llama-220M-openhermes.Q6_K.gguf) | Q6_K | 0.17GB |
| [smol_llama-220M-openhermes.Q8_0.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-openhermes-gguf/blob/main/smol_llama-220M-openhermes.Q8_0.gguf) | Q8_0 | 0.22GB |
Original model description:
---
license: apache-2.0
datasets:
- teknium/openhermes
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: 1.03
no_repeat_ngram_size: 5
epsilon_cutoff: 0.0008
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
model-index:
- name: smol_llama-220M-openhermes
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 25.17
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-openhermes
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 28.98
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-openhermes
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 26.17
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-openhermes
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 43.08
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-openhermes
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 52.01
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-openhermes
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 0.61
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-openhermes
name: Open LLM Leaderboard
---
# 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
---
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_BEE-spoke-data__smol_llama-220M-openhermes)
| Metric |Value|
|---------------------------------|----:|
|Avg. |29.34|
|AI2 Reasoning Challenge (25-Shot)|25.17|
|HellaSwag (10-Shot) |28.98|
|MMLU (5-Shot) |26.17|
|TruthfulQA (0-shot) |43.08|
|Winogrande (5-shot) |52.01|
|GSM8k (5-shot) | 0.61|
|