Update README.md

README.md

@@ -1,3 +1,61 @@
 ---
 license: apache-2.0
+datasets:
+- cerebras/SlimPajama-627B
+- bigcode/starcoderdata
+- teknium/openhermes
+language:
+- en
 ---
+Was testing this model out and found it pretty decent for a 1.1B model. Smaller models are still stupid but can work as a conversational partner on low-end hardware.
+
+Was inspired after hearing about it on r/LocalLLaMA and finding out the only other quants of this model are Q4_K_M and Q8_0.
+
+I also tried converting it to IQ2_XXS, IQ2_XS, and Q2_K_S, but none of them worked because I need importance matrix.
+
+Original model card below.
+***
+# TinyDolphin-2.8.2-1.1b-laser
+
+![image/webp](https://cdn-uploads.huggingface.co/production/uploads/655dc641accde1bbc8b41aec/x8c5Ue58EAHRl1cp2Wwk1.webp)
+
+Join Our Discord! https://discord.gg/cognitivecomputations
+
+This is an version 3 of a model trained on 3 3090's by Kearm on the new Dolphin 2.8 dataset by Eric Hartford https://erichartford.com/dolphin 🐬
+
+This model uses our laser technique from https://github.com/cognitivecomputations/laserRMT to denoise the model!  
+
+For this version we increased the epochs as well as refined the datasets used.
+
+## Example Outputs
+
+TBD
+
+Support my efforts! https://ko-fi.com/kearm
+
+# Orignal Model Card Below
+
+# TinyLlama-1.1B
+</div>
+
+https://github.com/jzhang38/TinyLlama
+
+The TinyLlama project aims to **pretrain** a **1.1B Llama model on 3 trillion tokens**. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01. 
+
+We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.
+
+#### This Collection
+This collection contains all checkpoints after the 1T fix. Branch name indicates the step and number of tokens seen.
+
+#### Eval
+
+| Model                                     | Pretrain Tokens | HellaSwag | Obqa | WinoGrande | ARC_c | ARC_e | boolq | piqa | avg |
+|-------------------------------------------|-----------------|-----------|------|------------|-------|-------|-------|------|-----|
+| Pythia-1.0B                               |        300B     | 47.16     | 31.40| 53.43      | 27.05 | 48.99 | 60.83 | 69.21 | 48.30 |
+| TinyLlama-1.1B-intermediate-step-50K-104b |        103B     | 43.50     | 29.80| 53.28      | 24.32 | 44.91 | 59.66 | 67.30 | 46.11|
+| TinyLlama-1.1B-intermediate-step-240k-503b|        503B     | 49.56     |31.40 |55.80       |26.54  |48.32  |56.91  |69.42  | 48.28 |
+| TinyLlama-1.1B-intermediate-step-480k-1007B |     1007B     | 52.54     | 33.40 | 55.96      | 27.82 | 52.36 | 59.54 | 69.91 | 50.22 |
+| TinyLlama-1.1B-intermediate-step-715k-1.5T |     1.5T     | 53.68     | 35.20 | 58.33      | 29.18 | 51.89 | 59.08 | 71.65 | 51.29 |
+| TinyLlama-1.1B-intermediate-step-955k-2T |     2T     | 54.63     | 33.40 | 56.83      | 28.07 | 54.67 | 63.21 | 70.67 | 51.64 |
+| TinyLlama-1.1B-intermediate-step-1195k-2.5T              |     2.5T     | 58.96     | 34.40 | 58.72      | 31.91 | 56.78 | 63.21 | 73.07 | 53.86|
+| TinyLlama-1.1B-intermediate-step-1431k-3T |     3T     | 59.20     | 36.00 | 59.12      | 30.12 | 55.25 | 57.83 | 73.29 | 52.99|