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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/
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:
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
Detailed results can be found here
| 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 |
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