Update README.md

README.md

@@ -8,17 +8,34 @@ tags:
 - nlp
 - llm
 ---
-# K2: a Fully Transparent OSS Language at Llama 2 Performance Using 35% Less Compute
+# K2: a Fully Reproducible Large Language Model Outperforming Llama 2 70B using 35% less compute
 
-LLM360 demystifies the training recipe used for Llama 2 - 70B with K2. Reaching a comparable performance with Llama 2, K2 has 65B parameters 
-and is trained on around 1.4T tokens, resulting a receipe of approximately 35% less compute.
+LLM360 demystifies the training recipe used for Llama 2 - 70B with K2.
+
+About K2:
+* 65 billion parameter LLM
+* Tokens: 1.4T
+* Languages: English
+* Models Released: base, chat model
+* Trained in 2 stages
+* License: Apache 2.0
+
+K2 was developed as a collaboration between [MBZUAI](https://mbzuai.ac.ae/institute-of-foundation-models/), [Petuum](petuum.com), and [LLM360](llm360.ai).
+
+## LLM360 Model Performance and Evaluation Collection
+
+The LLM360 Performance and Evaluation Collection is a robust evaluations set consisting of general and domain specific evaluations to assess model knowledge and function. Evaluations include standard best practice benchmarks, medical, math, and coding knowledge. More about the evaluations can be found [here](llm360.ai/evaluations).
+
+put the general table in here
+
+Detailed analysis can be found on the K2 Weights and Biases project [here](wandb.ai)
 
-## Evaluations
-<center><img src="eval_table_temp.png" alt="eval table"/></center>
 
 ## Datasets and Mix
 
-The following data mix was used to train K2 and achieve results in line with Llama 2 70B. The full data sequence will be available soon. 
+The following data mix was used to train K2 and achieve results in line with Llama 2 70B. 
+
+The full data sequence can be found [here](https://huggingface.co/datasets/LLM360/K2Datasets/tree/main) 
 
 | Dataset      | Starting Tokens      | Multiplier      | Total Tokens      |% of Total      |
 | ----------- | ----------- | ----------- | ----------- | ----------- |
@@ -38,7 +55,19 @@ The following data mix was used to train K2 and achieve results in line with Lla
 | [refinedweb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)   | 612.3B        | 1x       | 612.3B       | 47.1%       |
 | Totals   | -        | -       | 1.3T       | 100%       |
 
-## First 10 Checkpoints
+
+# LLM360 Reasearch Suite
+
+## Stage 2 - Last 10 Checkpoints
+| Checkpoints      |  |
+| ----------- | ----------- |
+| [Checkpoint 380](https://huggingface.co/LLM360/K2/tree/ministage2_ckpt_380)     | [Checkpoint 375](https://huggingface.co/LLM360/K2/tree/ministage2_ckpt_375)       |
+| [Checkpoint 379](https://huggingface.co/LLM360/K2/tree/ministage2_ckpt_379)   | [Checkpoint 374](https://huggingface.co/LLM360/K2/tree/ministage2_ckpt_374)        |
+| [Checkpoint 378](https://huggingface.co/LLM360/K2/tree/ministage2_ckpt_378)   | [Checkpoint 373](https://huggingface.co/LLM360/K2/tree/ministage2_ckpt_373)        |
+| [Checkpoint 377](https://huggingface.co/LLM360/K2/tree/ministage2_ckpt_377)   | [Checkpoint 372](https://huggingface.co/LLM360/K2/tree/ministage2_ckpt_372)        |
+| [Checkpoint 376](https://huggingface.co/LLM360/K2/tree/ministage2_ckpt_376)   | [Checkpoint 371](https://huggingface.co/LLM360/K2/tree/ministage2_ckpt_371)        |
+
+## Stage 1 - Last 10 Checkpoints
 | Checkpoints      |  |
 | ----------- | ----------- |
 | [Checkpoint 360](https://huggingface.co/LLM360/K2/tree/ckpt_360)     | [Checkpoint 355](https://huggingface.co/LLM360/K2/tree/ckpt_355)       |
@@ -49,8 +78,13 @@ The following data mix was used to train K2 and achieve results in line with Lla
 
 [to find all branches: git branch -a]
 
-## Additional Artifacts
-We are working on release caliber artifacts for the dataset, code, and analysis which will be released over the next few weeks.
+## LLM360 Pretraining Suite
+We provide step-by-step reproducation tutorials for tech enthusiasts, AI practitioners and academic or industry researchers who want to learn pretraining techniques here [llm360.ai/pretraining]
+
+## LLM360 Developer Suite
+We provide step-by-step finetuning tutorials for tech enthusiasts, AI practitioners and academic or industry researchers here [llm360.ai/pretraining].
+
+#Put inference section here.
 
 
 ## Model Description