Nous-Hermes-2-SUS-Chat-34B-Slerp
This is the model for Nous-Hermes-2-SUS-Chat-34B-Slerp. I used mergekit to merge models.
Prompt Templates
You can use these prompt templates, but I recommend using ChatML.
ChatML (NousResearch/Nous-Hermes-2-Yi-34B):
<|im_start|>system
{system}<|im_end|>
<|im_start|>user
{user}<|im_end|>
<|im_start|>assistant
{asistant}<|im_end|>
Human - Asistant (SUSTech/SUS-Chat-34B):
### Human: {user}
### Assistant: {asistant}
Yaml Config
slices:
- sources:
- model: Nous-Hermes-2-Yi-34B
layer_range: [0, 60]
- model: SUS-Chat-34B
layer_range: [0, 60]
merge_method: slerp
base_model: Yi-34B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
tokenizer_source: union
dtype: bfloat16
Quantizationed versions
Quantizationed versions of this model is available thanks to TheBloke.
GPTQ
GGUF
AWQ
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 74.06 |
AI2 Reasoning Challenge (25-Shot) | 66.72 |
HellaSwag (10-Shot) | 84.97 |
MMLU (5-Shot) | 77.00 |
TruthfulQA (0-shot) | 59.23 |
Winogrande (5-shot) | 83.58 |
GSM8k (5-shot) | 72.86 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard66.720
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard84.970
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard77.000
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard59.230
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard83.580
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard72.860