---
language:
- en
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
model-index:
- name: neuronovo-7B-v0.1
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 66.98
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Neuronovo/neuronovo-7B-v0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 85.07
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Neuronovo/neuronovo-7B-v0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 63.33
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Neuronovo/neuronovo-7B-v0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 53.95
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Neuronovo/neuronovo-7B-v0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 78.14
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Neuronovo/neuronovo-7B-v0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 37.68
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Neuronovo/neuronovo-7B-v0.1
      name: Open LLM Leaderboard
---
The model described by the provided code, named "Neuronovo/neuronovo-9B-v0.1," is a sophisticated and fine-tuned version of a large language model, originally based on the "teknium/OpenHermes-2.5-Mistral-7B." This model exhibits several distinct characteristics and functionalities as derived from the code snippet:

1. **Dataset and Preprocessing**: It is trained on a dataset named "Intel/orca_dpo_pairs," which is likely a specialized dataset for dialogue systems. The data is preprocessed to format dialogues, with specific attention to system messages, user queries, chosen answers, and rejected answers.

2. **Tokenizer**: The model utilizes a tokenizer from the original "OpenHermes-2.5-Mistral-7B" model. This tokenizer is configured to have the end-of-sequence token as the padding token and pads from the left, indicating a particular focus on language generation tasks.

3. **LoRA Configuration**: The model employs a LoRA (Low-Rank Adaptation) configuration with specific parameters (r=16, lora_alpha=16, etc.) and targets multiple modules within the transformer architecture. This suggests an approach focused on efficient fine-tuning and adaptation of the model while preserving the majority of the pre-trained weights.

4. **Fine-Tuning Specifications**: The model is fine-tuned using a custom training setup, including a special DPO (Data Parallel Optimization) Trainer. This indicates an advanced fine-tuning process that likely emphasizes both efficiency and effectiveness, possibly with a focus on parallel processing and optimization.

5. **Training Arguments**: The training uses specific arguments like a cosine learning rate scheduler, paged AdamW optimizer, and training in 4-bit precision (indicating a focus on memory efficiency). It also employs gradient checkpointing and accumulation steps, which are typical in training large models efficiently.

6. **Performance and Output**: The model is configured for causal language modeling (indicative of generating text or continuing dialogues), with a maximum prompt length of 1024 and maximum generation length of 1536 tokens. This setup suggests its capability for handling extended dialogues or text generation tasks.

7. **Special Features**: The use of LoRA, DPO training, and specific fine-tuning methods highlight the model's advanced capabilities in adapting large-scale language models to specific tasks or datasets while maintaining computational efficiency.

In summary, "Neuronovo/neuronovo-9B-v0.1" is a highly specialized, efficient, and capable large language model fine-tuned for advanced language generation tasks, particularly in the context of dialogues or interactions, leveraging cutting-edge techniques in NLP model adaptation and training.



---
license: apache-2.0
---
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Neuronovo__neuronovo-7B-v0.1)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |64.19|
|AI2 Reasoning Challenge (25-Shot)|66.98|
|HellaSwag (10-Shot)              |85.07|
|MMLU (5-Shot)                    |63.33|
|TruthfulQA (0-shot)              |53.95|
|Winogrande (5-shot)              |78.14|
|GSM8k (5-shot)                   |37.68|