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

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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

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	4.76
IFEval (0-Shot)	15.55
BBH (3-Shot)	3.11
MATH Lvl 5 (4-Shot)	0.00
GPQA (0-shot)	2.35
MuSR (0-shot)	6.22
MMLU-PRO (5-shot)	1.34