Mention and thank other quants, tidy README

README.md

@@ -78,7 +78,7 @@ A quick overview of the model's strengths include:
 - Strong system prompt adherence
 - Best-in-class multilingual capabilities compared to competing models of its size (English, Chinese, Spanish, French, Polish, and more!)
 - Being unbiased and truthful (although, you should note that all forms of intelligence can and WILL make mistakes, whether organic or artificial)
-- Having no unecessary censorship (some unfortunately bleeds through since `Meta-Llama-3-8B-Instruct` was used as a base and version 3 should fix that)
+- Having no unecessary censorship (some unfortunately bleeds through since `Meta-Llama-3-8B-Instruct` was used as a base and HuskyLM 3 should fix that - we're training from the ground up from the base Llama 3 next time)
 - Simply being fun to talk to
 
 ## Model Specs
@@ -94,13 +94,16 @@ A quick overview of the model's strengths include:
 Well, yes, but actually no. You may see the names of benchmarks in the datasets used, however only **train** splits were used. If you don't know the difference, please learn.
 
 ## Quants and Other Formats
-- GGUFs: [https://huggingface.co/darkcloudai/huskylm-2.5-8b-GGUF](https://huggingface.co/darkcloudai/huskylm-2.5-8b-GGUF)
-- AWQ (bits: 4, gs: 128, version: gemm): [https://huggingface.co/darkcloudai/huskylm-2.5-8b-AWQ](https://huggingface.co/darkcloudai/huskylm-2.5-8b-AWQ)
+- GGUFs:
+    * Official: [https://huggingface.co/darkcloudai/huskylm-2.5-8b-GGUF](https://huggingface.co/darkcloudai/huskylm-2.5-8b-GGUF)
+    * mradermacher's static quants (thank you!): [https://huggingface.co/mradermacher/huskylm-2.5-8b-GGUF](https://huggingface.co/mradermacher/huskylm-2.5-8b-GGUF)
+    * mradermacher's imatrix quants (thank you!): [https://huggingface.co/mradermacher/huskylm-2.5-8b-i1-GGUF](https://huggingface.co/mradermacher/huskylm-2.5-8b-i1-GGUF)
+- Official AWQ (bits: 4, gs: 128, version: gemm): [https://huggingface.co/darkcloudai/huskylm-2.5-8b-AWQ](https://huggingface.co/darkcloudai/huskylm-2.5-8b-AWQ)
 
 ## Huge Thank You to the Following People/Companies
 
 - [Meta AI](https://llama.meta.com/llama3/): This model would never have been possible if Meta AI did not release Llama 3 with an open license. We thank them deeply for making frontier LLMs available for all.
-- [Jon Durbin](https://huggingface.co/jondurbin): We've used many of his datasets to train this model, specifically `airoboros-3.2`, `contextual-dpo-v0.1`, `gutenberg-dpo-v0.1`, `py-dpo-v0.1`, `truthy-dpo-v0.1`, `cinematika-v0.1`, `gutenberg-dpo-v0.1`. His work is amazing and we thank him a lot. We've used a lot of datasets for our model that he used for his `bagel` series of models too. If you couldn't already guess, this model is essentially a `bagel` model but with our custom datasets and RLAIF methodology added in.
+- [Jon Durbin](https://huggingface.co/jondurbin): We've used many of his datasets to train this model, specifically `airoboros-3.2`, `contextual-dpo-v0.1`, `gutenberg-dpo-v0.1`, `py-dpo-v0.1`, `truthy-dpo-v0.1`, `cinematika-v0.1`, `gutenberg-dpo-v0.1`. His work is amazing and we thank him a lot. We've used a lot of datasets for our model that he used for his `bagel` series of models too. If you couldn't already guess, this model is essentially a `bagel`-type model but with our custom datasets and RLAIF methodology added in.
 - [Hugging Face](https://github.com/huggingface): Throughout Darkcloud AI's life, we've extensively used and relied on libraries made by HuggingFace and we thank them and everyone who has contributed.
 - [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl): We've used Axolotl to streamline the (SFT) fine-tuning of our LLMs. Huge thank you to them and every contributor.
 - You: That's right! You, the user. We value every single bit of feedback we receive from our users as it helps us to make our models better for everyone. If you have any issues, *please* give feedback. Every little bit of information helps, no matter how minor the issue or question you have is!