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---
license: mit
language:
- en
base_model:
- unsloth/Phi-3-mini-4k-instruct
datasets:
- cognitivecomputations/Dolphin-2.9
- teknium/OpenHermes-2.5
- m-a-p/CodeFeedback-Filtered-Instruction
- cognitivecomputations/dolphin-coder
- cognitivecomputations/samantha-data
- microsoft/orca-math-word-problems-200k
- Locutusque/function-calling-chatml
- internlm/Agent-FLAN
---
# AWQ quantization of Dolphin-2.9.1-Phi-3-Kensho-4.5B
See more below.
# Dolphin 2.9.1 Phi-3 Kensho 4.5b 🐬
Curated and trained by Eric Hartford, Lucas Atkins, Fernando Fernandes, and with help from the community of Cognitive Computations
[](https://discord.gg/cognitivecomputations)
Discord: https://discord.gg/cognitivecomputations
<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" />
Our appreciation for the sponsors of Dolphin 2.9:
- [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 8xL40Snode
This model utilizes PEFT layer replication at inference time to duplicate layers and increase parameter count. This works with both the merged model that comes stock with this repository,
and the adapter that is attached as well. Performance will be similar with both methods, but VRAM use is considerably less when using the adapter.
This model was initialized using [Unsloth's Mistralfied Phi-3-Instruct-4k](https://huggingface.co/unsloth/Phi-3-mini-4k-instruct). If you choose to use the adapter method, please attach it to their model.
<img src="https://i.ibb.co/C6sqLBH/Vram-Use.png" width="300">
This model is based on Phi-3-Mini-Instruct-4k, and is governed by the MIT license in which Microsoft released Phi-3.
The base model has 4k context, and the qLoRA fine-tuning was with 4k sequence length.
It took 2.5 days on 8xL40S node provided by Crusoe Cloud
This model uses ChatML prompt template format.
example:
```
<|im_start|>system
You are Dolphin, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
Dolphin-2.9.1 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling.
We have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly.
Dolphin is licensed according to the MIT license. I grant permission for any use, including commercial. Dolphin was trained on data generated from GPT4, among other models.
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
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