MagnusIntellectus-12B-v1
How pleasant, the rocks appear to have made a decent conglomerate. A-.
MagnusIntellectus is a merge of the following models using LazyMergekit:
𧩠Configuration
models:
- model: UsernameJustAnother/Nemo-12B-Marlin-v5
parameters:
density: 0.4
weight: 0.70
- model: anthracite-org/magnum-12b-v2
parameters:
density: 0.6
weight: 0.30
merge_method: ties
base_model: UsernameJustAnother/Nemo-12B-Marlin-v5
parameters:
normalize: true
dtype: bfloat16
π» Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "GalrionSoftworks/MagnusIntellectus-12B-v1"
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. | 21.55 |
IFEval (0-Shot) | 44.21 |
BBH (3-Shot) | 33.26 |
MATH Lvl 5 (4-Shot) | 5.14 |
GPQA (0-shot) | 4.59 |
MuSR (0-shot) | 15.18 |
MMLU-PRO (5-shot) | 26.90 |
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Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard44.210
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard33.260
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard5.140
- acc_norm on GPQA (0-shot)Open LLM Leaderboard4.590
- acc_norm on MuSR (0-shot)Open LLM Leaderboard15.180
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard26.900