README.md

---
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
---

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    <a href="https://jan.ai/">Jan</a
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    - <a href="https://discord.gg/AsJ8krTT3N">Discord</a>
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# 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|