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--- |
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license: apache-2.0 |
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base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T |
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tags: |
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- alignment-handbook |
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- generated_from_trainer |
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- trl |
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- sft |
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- generated_from_trainer |
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datasets: |
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- jan-hq/bagel_sft_binarized |
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- jan-hq/dolphin_binarized |
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- jan-hq/openhermes_binarized |
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model-index: |
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- name: LlamaCorn-sft-adapter |
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results: [] |
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--- |
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<!-- header start --> |
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<!-- 200823 --> |
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<div style="width: auto; margin-left: auto; margin-right: auto" |
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> |
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<img src="https://github.com/janhq/jan/assets/89722390/35daac7d-b895-487c-a6ac-6663daaad78e" alt="Jan banner" |
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style="width: 100%; min-width: 400px; display: block; margin: auto;"> |
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</div> |
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<p align="center"> |
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<a href="https://jan.ai/">Jan</a |
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> |
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- <a href="https://discord.gg/AsJ8krTT3N">Discord</a> |
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</p> |
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<!-- header end --> |
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# Prompt template |
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ChatML |
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``` |
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<|im_start|>system |
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{system_message}<|im_end|> |
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<|im_start|>user |
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{prompt}<|im_end|> |
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<|im_start|>assistant |
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``` |
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# Run this model |
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You can run this model using [Jan Desktop](https://jan.ai/) on Mac, Windows, or Linux. |
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Jan is an open source, ChatGPT alternative that is: |
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- 💻 **100% offline on your machine**: Your conversations remain confidential, and visible only to you. |
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- 🗂️ ** |
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An Open File Format**: Conversations and model settings stay on your computer and can be exported or deleted at any time. |
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- 🌐 **OpenAI Compatible**: Local server on port `1337` with OpenAI compatible endpoints |
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- 🌍 **Open Source & Free**: We build in public; check out our [Github](https://github.com/janhq) |
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# About Jan |
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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. |
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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. |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# LlamaCorn-sft-adapter |
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This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) on the jan-hq/bagel_sft_binarized, the jan-hq/dolphin_binarized and the jan-hq/openhermes_binarized datasets. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9638 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 7e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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- total_eval_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| 1.038 | 1.0 | 6606 | 1.0506 | |
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| 0.876 | 2.0 | 13212 | 0.9648 | |
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| 0.7713 | 3.0 | 19818 | 0.9638 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.14.6 |
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- Tokenizers 0.15.0 |
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