---
license: apache-2.0
tags:
- alignment-handbook
- generated_from_trainer
- trl
- sft
- generated_from_trainer
datasets:
- jan-hq/bagel_sft_binarized
- jan-hq/dolphin_binarized
- jan-hq/openhermes_binarized
- jan-hq/bagel_dpo_binarized
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
pipeline_tag: text-generation
inference:
parameters:
temperature: 0.7
max_new_tokens: 40
widget:
- messages:
- role: user
content: Tell me about NVIDIA in 20 words
---
Jan
- Discord
# Model description
- Finetuned [TinyLlama-1.1B](TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) further for handling simple tasks and have acceptable conversational quality
- Utilized high-quality opensource dataset
- Can be run on [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) on consumer devices
- Can fit into laptop dGPUs with as little as >=6gb of VRAM
# Prompt template
ChatML
```
<|im_start|>system
{system_message}<|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)
![image/png](https://cdn-uploads.huggingface.co/production/uploads/65713d70f56f9538679e5a56/r7VmEBLGXpPLTu2MImM7S.png)
# 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.
# LlamaCorn-1.1B-Chat
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:-----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.9958 | 0.03 | 100 | 1.0003 | -0.0002 | -0.0002 | 0.4930 | -0.0001 | -180.9232 | -195.6078 | -2.6876 | -2.6924 |
| 0.9299 | 1.02 | 3500 | 0.9439 | -0.1570 | -0.2195 | 0.5770 | 0.0625 | -183.1160 | -197.1755 | -2.6612 | -2.6663 |
| 0.9328 | 2.01 | 6900 | 0.9313 | -0.2127 | -0.2924 | 0.5884 | 0.0798 | -183.8456 | -197.7321 | -2.6296 | -2.6352 |
| 0.9321 | 2.98 | 10200 | 0.9305 | -0.2149 | -0.2955 | 0.5824 | 0.0805 | -183.8759 | -197.7545 | -2.6439 | -2.6493 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.0
# [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_jan-hq__LlamaCorn-1.1B)
| Metric |Value|
|---------------------------------|----:|
|Avg. |36.94|
|AI2 Reasoning Challenge (25-Shot)|34.13|
|HellaSwag (10-Shot) |59.33|
|MMLU (5-Shot) |29.01|
|TruthfulQA (0-shot) |36.78|
|Winogrande (5-shot) |61.96|
|GSM8k (5-shot) | 0.45|