---
language:
- en
license: mit
model-index:
- name: lil-c3po
  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: 65.02
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=deepnight-research/lil-c3po
      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: 84.45
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=deepnight-research/lil-c3po
      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: 62.36
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=deepnight-research/lil-c3po
      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: 68.73
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=deepnight-research/lil-c3po
      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: 79.16
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=deepnight-research/lil-c3po
      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: 48.45
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=deepnight-research/lil-c3po
      name: Open LLM Leaderboard
---

# deepnight-research/lil-c3po
<div style="display: flex; justify-content: center; align-items: center;">
<img src="./lil-c3po.jpg" style="width: 100%; height: auto;"/></div>

## Model Details:
lil-c3po is an open-source large language model (LLM) resulting from the linear merge of two distinct
fine-tuned Mistral-7B models, internally referred to as c3-1 and c3-2. These models, developed in-house, 
bring together unique characteristics to enhance performance and utility.

## Model Architecture:
lil-c3po inherits its architecture from the combined c3-1 and c3-2 models, 
incorporating features such as Grouped-Query Attention, Sliding-Window Attention, and Byte-fallback BPE tokenizer. 
This fusion aims to capitalize on the strengths of both models for improved language understanding and generation.

## Training Details:
- The first model, internally referred to as c3-1, is a 7B parameter Large Language Model
fine-tuned on the Intel Gaudi 2 processor.
It utilizes the Direct Performance Optimization (DPO) method and is designed to excel in various language-related tasks.
- The second model, denoted as c3-2, is an instruct fine-tuned version of Mistral-7B.
Its architecture features improvements in instruct fine-tuning, contributing to enhanced language understanding in instructional contexts.

## License:
lil-c3po is released under the MIT license, fostering open-source collaboration and innovation.

## Intended Use:
This merged model is suitable for a broad range of language-related tasks,
inheriting the capabilities of the fine-tuned c3-1 and c3-2 models. Users interested in language tasks can leverage lil-c3po's capabilities.

## Out-of-Scope Uses:
While lil-c3po is versatile, it is important to note that, in most cases, fine-tuning may be necessary for specific tasks.
Additionally, the model should not be used to intentionally create hostile or alienating environments for people.
# [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_deepnight-research__lil-c3po)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |68.03|
|AI2 Reasoning Challenge (25-Shot)|65.02|
|HellaSwag (10-Shot)              |84.45|
|MMLU (5-Shot)                    |62.36|
|TruthfulQA (0-shot)              |68.73|
|Winogrande (5-shot)              |79.16|
|GSM8k (5-shot)                   |48.45|