metadata
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- cognitivecomputations/dolphin-2.8-experiment26-7b
- argilla/CapybaraHermes-2.5-Mistral-7B
base_model:
- cognitivecomputations/dolphin-2.8-experiment26-7b
- argilla/CapybaraHermes-2.5-Mistral-7B
language:
- en
- de
- es
- fr
- ja
- zh
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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"harness|hendrycksTest-public_relations|5": {
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"harness|hendrycksTest-security_studies|5": {
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"harness|hendrycksTest-us_foreign_policy|5": {
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"harness|hendrycksTest-virology|5": {
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"harness|hendrycksTest-world_religions|5": {
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"acc_norm": 0.8187134502923976,
"acc_norm_stderr": 0.029547741687640038
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"harness|truthfulqa:mc|0": {
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"mc2": 0.5576866593959974,
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"harness|winogrande|5": {
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"acc_stderr": 0.011477747684223188
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"harness|gsm8k|5": {
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}
💻 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"])