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---
license: apache-2.0
base_model: jan-hq/LlamaCorn-1.1B-Chat
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
datasets:
- jan-hq/systemchat_binarized
- jan-hq/youtube_transcripts_qa
- jan-hq/youtube_transcripts_qa_ext
model-index:
- name: TinyJensen-1.1B-Chat
results: []
pipeline_tag: text-generation
widget:
- messages:
- role: user
content: Tell me about NVIDIA in 20 words
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto"
>
<img src="https://github.com/janhq/jan/assets/89722390/35daac7d-b895-487c-a6ac-6663daaad78e" alt="Jan banner"
style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<p align="center">
<a href="https://jan.ai/">Jan</a
>
- <a
href="https://discord.gg/AsJ8krTT3N"
>Discord</a>
</p>
<!-- header end -->
# Model description
- Finetuned [LlamaCorn-1.1B-Chat](https://huggingface.co/jan-hq/LlamaCorn-1.1B-Chat) further to act like Jensen Huang - CEO of NVIDIA.
- Use this model with caution because it can make you laugh.
# Prompt template
ChatML
```
<|im_start|>system
You are Jensen Huang, CEO of NVIDIA<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
# Run this model
You can run this model using [Jan Desktop](https://jan.ai/) on Mac, Windows, or Linux.
Jan is an open source, ChatGPT alternative that is:
- 💻 **100% offline on your machine**: Your conversations remain confidential, and visible only to you.
- 🗂️ **
An Open File Format**: Conversations and model settings stay on your computer and can be exported or deleted at any time.
- 🌐 **OpenAI Compatible**: Local server on port `1337` with OpenAI compatible endpoints
- 🌍 **Open Source & Free**: We build in public; check out our [Github](https://github.com/janhq)
%3C%2Fspan%3E
# About Jan
Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones.
Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life.
# Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
# Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.8226 | 1.0 | 207 | 0.8232 |
| 0.6608 | 2.0 | 414 | 0.7941 |
| 0.526 | 3.0 | 621 | 0.8186 |
| 0.4388 | 4.0 | 829 | 0.8643 |
| 0.3888 | 5.0 | 1035 | 0.8771 |
# Framework versions
- Transformers 4.37.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.0
|