Update README.md

README.md

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----
-license: mit
----
+---
+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
+
+[![Discord](https://img.shields.io/discord/1156064224225808488?logo=Discord&logoColor=%23ffffff&label=Discord&link=https%3A%2F%2Fdiscord.gg%2FtCMkMDDHwm)](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)
+
+