YAML Metadata Warning: 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, 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, 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

Downloads last month: 408

Safetensors

Model size

7.24B params

Tensor type

BF16

Text Generation

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Merge of

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

69.280
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

86.600
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

64.960
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

62.570
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

80.820
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

72.180