yam-jom-7B
yam-jom-7B is a task arithmetic merge of the following models using LazyMergekit:
🧩 Configuration
models:
- model: eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v2
parameters:
weight: 0.35
- model: yam-peleg/Experiment26-7B
parameters:
weight: 0.65
base_model: yam-peleg/Experiment26-7B
merge_method: task_arithmetic
dtype: bfloat16
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mayacinka/yam-jom-7B"
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"])
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 76.60 |
AI2 Reasoning Challenge (25-Shot) | 73.38 |
HellaSwag (10-Shot) | 89.15 |
MMLU (5-Shot) | 64.51 |
TruthfulQA (0-shot) | 78.04 |
Winogrande (5-shot) | 84.93 |
GSM8k (5-shot) | 69.60 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard73.380
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard89.150
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard64.510
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard78.040
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard84.930
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard69.600