Steelskull/Etheria-55b-v0.1
Merge Details
An attempt to make a functional goliath style merge to create a [Etheria] 55b-200k with two yi-34b-200k models.
due to the merge it 'theoretically' should have a context of 200k but I recommend starting at 32k and moveing up, as it is unknown (at this time) what the merge has done to the context length.
This is a merge of both VerA and VerB of Etheria-55b (There numbers were surprisingly good), I then created a sacrificial 55B out of the most performant yi-34b-200k Model and performed a Dare_ties merge and equalize the model into its current state.
recommended settings and Prompt Format:
Ive tested it up to 32k context using exl2 using these settings:
"temp": 0.7,
"temperature_last": true,
"top_p": 1,
"top_k": 0,
"top_a": 0,
"tfs": 1,
"epsilon_cutoff": 0,
"eta_cutoff": 0,
"typical_p": 1,
"min_p": 0.1,
"rep_pen": 1.1,
"rep_pen_range": 8192,
"no_repeat_ngram_size": 0,
"penalty_alpha": 0,
"num_beams": 1,
"length_penalty": 1,
"min_length": 0,
"encoder_rep_pen": 1,
"freq_pen": 0,
"presence_pen": 0,
"do_sample": true,
"early_stopping": false,
"add_bos_token": false,
"truncation_length": 2048,
"ban_eos_token": true,
"skip_special_tokens": true,
"streaming": true,
"mirostat_mode": 0,
"mirostat_tau": 5,
"mirostat_eta": 0.1,
Prompt format that work well
ChatML & Alpaca
Merge Method
This model was merged using the DARE TIES merge method using Merged-Etheria-55b as a base.
Configuration
The following YAML configuration was used to produce this model:
base_model: Merged-Etheria-55b
models:
- model: Sacr-Etheria-55b
parameters:
weight: [0.22, 0.113, 0.113, 0.113, 0.113, 0.113]
density: 0.61
- model: Merged-Etheria-55b
parameters:
weight: [0.22, 0.113, 0.113, 0.113, 0.113, 0.113]
density: 0.61
merge_method: dare_ties
tokenizer_source: union
parameters:
int8_mask: true
dtype: bfloat16
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 64.69 |
AI2 Reasoning Challenge (25-Shot) | 65.10 |
HellaSwag (10-Shot) | 81.93 |
MMLU (5-Shot) | 73.66 |
TruthfulQA (0-shot) | 56.16 |
Winogrande (5-shot) | 76.09 |
GSM8k (5-shot) | 35.18 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard65.100
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard81.930
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard73.660
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard56.160
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard76.090
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard35.180