|
--- |
|
license: mit |
|
language: |
|
- en |
|
--- |
|
|
|
# deepnight-research/lil-c3po |
|
<div style="display: flex; justify-content: center; align-items: center;"> |
|
<img src="./lil-c3po.jpg" style="width: 100%; max-width: 350px; 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, specifically tailored for Intel architecture, 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. |
