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The pipeline tag "conversational" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, any-to-any, other
Oot-v2_lll
Oot-v2_lll is a merge of the following models using Mergekit:
🧩 Configuration
slices:
- sources:
- model: mlabonne/Marcoro14-7B-slerp
layer_range: [0, 32]
- model: Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp
layer_range: [0, 32]
merge_method: slerp
base_model: mlabonne/Marcoro14-7B-slerp
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
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "damerajee/Oot-v2_lll"
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. | 72.73 |
| AI2 Reasoning Challenge (25-Shot) | 69.28 |
| HellaSwag (10-Shot) | 86.60 |
| MMLU (5-Shot) | 64.96 |
| TruthfulQA (0-shot) | 62.57 |
| Winogrande (5-shot) | 80.82 |
| GSM8k (5-shot) | 72.18 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard69.280
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard86.600
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard64.960
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard62.570
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard80.820
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard72.180