J.O.S.I.E.3-Beta11-7B-slerp
J.O.S.I.E.3-Beta11-7B-slerp is a merge of the following models using LazyMergekit:
Run in ollama:
ollama run goekdenizguelmez/j.o.s.i.e.v3-beta11
Only Quant 4-k-m for now!
This model will bee further Finetuned on my custom J.O.S.I.E.v3.13 Dataset, in the ChatML prompt Format.
<|im_start|>system
You are JOSIE, a private and super-intelligent AI assistant, created by Gökdeniz Gülmez.<|im_end|>
<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
{{ .Response }}<|im_end|>
🧩 Configuration
slices:
- sources:
- model: cognitivecomputations/dolphin-2.8-experiment26-7b
layer_range: [0, 32]
- model: argilla/CapybaraHermes-2.5-Mistral-7B
layer_range: [0, 32]
merge_method: slerp
base_model: argilla/CapybaraHermes-2.5-Mistral-7B
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
dtype: bfloat16
Evaluation
{
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"acc_norm": 0.6413927640714372,
"acc_norm_stderr": 0.03294011331780708,
"mc1": 0.39167686658506734,
"mc1_stderr": 0.017087795881769622,
"mc2": 0.5576866593959974,
"mc2_stderr": 0.01554622060467735
},
"harness|arc:challenge|25": {
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"acc_stderr": 0.014194389086685244,
"acc_norm": 0.6450511945392492,
"acc_norm_stderr": 0.013983036904094087
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"harness|hellaswag|10": {
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"acc_norm": 0.8499302927703645,
"acc_norm_stderr": 0.003564098420387764
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"acc_norm": 0.28,
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"harness|hendrycksTest-anatomy|5": {
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"harness|hendrycksTest-astronomy|5": {
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"acc_norm": 0.6907894736842105,
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"harness|hendrycksTest-business_ethics|5": {
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"harness|hendrycksTest-clinical_knowledge|5": {
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"harness|hendrycksTest-college_biology|5": {
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"harness|hendrycksTest-college_chemistry|5": {
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"harness|hendrycksTest-college_computer_science|5": {
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"harness|hendrycksTest-college_mathematics|5": {
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"harness|hendrycksTest-college_medicine|5": {
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"harness|hendrycksTest-econometrics|5": {
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"harness|hendrycksTest-electrical_engineering|5": {
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"harness|hendrycksTest-elementary_mathematics|5": {
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"harness|hendrycksTest-formal_logic|5": {
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"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.33,
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"harness|hendrycksTest-high_school_biology|5": {
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"harness|hendrycksTest-high_school_chemistry|5": {
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"harness|hendrycksTest-high_school_computer_science|5": {
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"acc_norm": 0.68,
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"harness|hendrycksTest-high_school_european_history|5": {
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"harness|hendrycksTest-high_school_geography|5": {
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"harness|hendrycksTest-high_school_government_and_politics|5": {
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"harness|hendrycksTest-high_school_microeconomics|5": {
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"harness|hendrycksTest-high_school_physics|5": {
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"harness|hendrycksTest-high_school_statistics|5": {
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"acc_norm": 0.5138888888888888,
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"harness|hendrycksTest-high_school_us_history|5": {
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"harness|hendrycksTest-high_school_world_history|5": {
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"acc_norm": 0.8143459915611815,
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"harness|hendrycksTest-human_aging|5": {
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"harness|hendrycksTest-human_sexuality|5": {
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"harness|hendrycksTest-international_law|5": {
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"harness|hendrycksTest-jurisprudence|5": {
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"harness|hendrycksTest-logical_fallacies|5": {
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"harness|hendrycksTest-management|5": {
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"harness|hendrycksTest-miscellaneous|5": {
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"harness|hendrycksTest-moral_disputes|5": {
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"acc_norm": 0.30726256983240224,
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"harness|hendrycksTest-nutrition|5": {
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"harness|hendrycksTest-philosophy|5": {
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"acc_norm": 0.7138263665594855,
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"harness|hendrycksTest-prehistory|5": {
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"acc_norm": 0.7283950617283951,
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"harness|hendrycksTest-professional_accounting|5": {
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"acc_norm": 0.48936170212765956,
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"harness|hendrycksTest-professional_law|5": {
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"harness|hendrycksTest-professional_medicine|5": {
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"acc_stderr": 0.028418208619406762,
"acc_norm": 0.6764705882352942,
"acc_norm_stderr": 0.028418208619406762
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"harness|hendrycksTest-professional_psychology|5": {
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"acc_stderr": 0.018926082916083376,
"acc_norm": 0.6764705882352942,
"acc_norm_stderr": 0.018926082916083376
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"harness|hendrycksTest-public_relations|5": {
"acc": 0.6636363636363637,
"acc_stderr": 0.04525393596302506,
"acc_norm": 0.6636363636363637,
"acc_norm_stderr": 0.04525393596302506
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"harness|hendrycksTest-security_studies|5": {
"acc": 0.7387755102040816,
"acc_stderr": 0.02812342933514278,
"acc_norm": 0.7387755102040816,
"acc_norm_stderr": 0.02812342933514278
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"harness|hendrycksTest-sociology|5": {
"acc": 0.835820895522388,
"acc_stderr": 0.02619392354445412,
"acc_norm": 0.835820895522388,
"acc_norm_stderr": 0.02619392354445412
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"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.83,
"acc_stderr": 0.0377525168068637,
"acc_norm": 0.83,
"acc_norm_stderr": 0.0377525168068637
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5421686746987951,
"acc_stderr": 0.0387862677100236,
"acc_norm": 0.5421686746987951,
"acc_norm_stderr": 0.0387862677100236
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8187134502923976,
"acc_stderr": 0.029547741687640038,
"acc_norm": 0.8187134502923976,
"acc_norm_stderr": 0.029547741687640038
},
"harness|truthfulqa:mc|0": {
"mc1": 0.39167686658506734,
"mc1_stderr": 0.017087795881769622,
"mc2": 0.5576866593959974,
"mc2_stderr": 0.01554622060467735
},
"harness|winogrande|5": {
"acc": 0.7884767166535123,
"acc_stderr": 0.011477747684223188
},
"harness|gsm8k|5": {
"acc": 0.6360879454131918,
"acc_stderr": 0.013252539227966195
}
}
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Isaak-Carter/J.O.S.I.E.3-Beta11-7B-slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
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