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MagnusIntellectus-12B-v1 - GGUF
- Model creator: https://huggingface.co/GalrionSoftworks/
- Original model: https://huggingface.co/GalrionSoftworks/MagnusIntellectus-12B-v1/
Original model description:
tags: - merge - mergekit - lazymergekit - UsernameJustAnother/Nemo-12B-Marlin-v5 - anthracite-org/magnum-12b-v2 base_model: - UsernameJustAnother/Nemo-12B-Marlin-v5 - anthracite-org/magnum-12b-v2 model-index: - name: MagnusIntellectus-12B-v1 results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 44.21 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=GalrionSoftworks/MagnusIntellectus-12B-v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 33.26 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=GalrionSoftworks/MagnusIntellectus-12B-v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 5.14 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=GalrionSoftworks/MagnusIntellectus-12B-v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 4.59 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=GalrionSoftworks/MagnusIntellectus-12B-v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 15.18 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=GalrionSoftworks/MagnusIntellectus-12B-v1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 26.9 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=GalrionSoftworks/MagnusIntellectus-12B-v1 name: Open LLM Leaderboard license: apache-2.0 pipeline_tag: text-generation library_name: transformers
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:
🧩 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
💻 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 |
Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more quants, at much higher speed, than I would otherwise be able to.
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