|
--- |
|
datasets: |
|
- jondurbin/airoboros-2.1 |
|
--- |
|
|
|
# Extended Context (via YaRN) Finetune of Llama-2-13b with airoboros-2.1 (fp16) |
|
|
|
|
|
## Overview |
|
|
|
This is a finetune of [NousResearch/Yarn-Llama-2-13b-64k](https://huggingface.co/NousResearch/Yarn-Llama-2-13b-64k). This starting point is Llama-2-13b with additional pretraining done with YaRN scaling applied to RoPE to extend the useful context length to 64k tokens. Starting with this model, I performed instruction tuning with [Jon Durbin's Airoboros 2.1 dataset](https://huggingface.co/datasets/jondurbin/airoboros-2.1), with same scaling approach applied. |
|
|
|
**This is a (merged) QLoRA fine-tune (rank 64)**. |
|
|
|
The finetune was performed with 1x RTX 6000 Ada (~18 hours). |
|
|
|
|
|
## How to Use |
|
|
|
YaRN is not implemented natively in `Transformers`. The YaRN pretrained model [NousResearch/Yarn-Llama-2-13b-64k](https://huggingface.co/NousResearch/Yarn-Llama-2-13b-64k) contains a drop-in llama architecture replacement that interfaces with the included configuration file. **To maximize compatibility, I have included the version that omits flash attention.** To run using `Transformers`, you will therefore need to pass `trust_remote_code=True`. |
|
|
|
The PNTK method employed in my other model [bhenrym14/airophin-13b-pntk-16k-fp16](https://huggingface.co/bhenrym14/airophin-13b-pntk-16k-fp16), is very similar to YaRN. For GPTQ, I have an exllama patch that I may adapt for YaRN, but the community appears motivated to rapidly implement YaRN in common libraries, so I may not bother. |
|
|
|
Please comment with any questions and feedback on how this model performs, especially at long context lengths! |
|
|
|
Ooba use: Be sure to increase the `Truncate the prompt up to this length` parameter to 16384 to utilize the full context capabilities. Again `trust_remote_code=True` is imperative |
|
|
|
## Motivation |
|
|
|
[Yet another RoPE extensioN method (YaRN)](https://github.com/jquesnelle/yarn) is a novel method of extending the useful context of pretrained LLMs, with architectures employing RoPE, with minimal additonal training requirements. This method is the consequence of efforts to mitigate the shortcomings of other methods such as Position Interpolation (PI) and NTK-Aware scaling. This model is an attempt to enable the community to assess the capabilities of this extension method in real world applications. |
|
|
|
## Relative Performance (wikitext perplexity) |
|
|
|
| Context (tokens) | **bhenrym14/airoboros-l2-13b-2.1-YaRN-64k** | bhenrym14/airoboros-l2-13b-PI-16k-fp16 | bhenrym14/airophin-v2-13b-PI-8k-fp16 | bhenrym14/airophin-13b-pntk-16k-fp16| bhenrym14/airoboros-13b-gpt4-1.4.1-PI-8192-fp16 |bhenrym14/airoboros-33b-gpt4-1.4.1-lxctx-PI-16384-fp16 | jondurbin/airoboros-l2-13b-gpt4-1.4.1 | |
|
| --- | --- |--- | ---| ----- | -----| ------| --- | |
|
| 512 | | 7.67 | 7.38 | 7.62 | 8.24 | 7.90 | **7.23** | |
|
| 1024 | | 6.15 | 5.99 | 6.20 | 6.71 | 6.17 | **5.85** | |
|
| 2048 | | 5.29 | 5.22 | 5.38 | 5.87 | 5.23 | **5.07** | |
|
| 4096 | |4.94 | 4.90 | 5.08 | 5.50 | 4.91 | **4.77** | |
|
| 8192 | |**4.71** | **4.71** | 4.90 | 5.32 | Not Tested | 57.1 | |
|
| 12000 | |**4.54** | 55 | 4.82 | 56.1 | Not Tested | Not Tested | |
|
|
|
|
|
## Prompting: |
|
|
|
Prompting differs with the airoboros 2.1 models. See [jondurbin/airoboros-l2-13b-2.1](https://huggingface.co/jondurbin/airoboros-l2-13b-2.1) |
