Update README.md

README.md

@@ -11,13 +11,17 @@ tags:
 Arctic is a Dense-MoE Hybrid transformer architecture pre-trained from scratch by the Snowflake AI 
 Research Team. We are releasing model checkpoints for both the base and instruct-tuned versions of 
 Arctic under an Apache-2.0 license. This means you can use them freely in your own research, 
-prototypes, and products. Please see our blog [Snowflake Arctic: Efficient Intelligence, Truly Open]() 
+prototypes, and products. Please see our blog 
+[Snowflake Arctic: The Best LLM for Enterprise AI — Efficiently Intelligent, Truly Open](https://www.snowflake.com/blog/arctic-open-and-efficient-foundation-language-models-snowflake) 
 for more information on Arctic and links to other relevant resources such as our series of cookbooks 
 covering topics around training your own custom MoE models, how to produce high-quality training data, 
 and much more.
 
-* [Arctic-Base](link-here)
-* [Acrtic-Instruct](link-to-instruct)
+* [Arctic-Base](https://huggingface.co/Snowflake/snowflake-arctic-base/)
+* [Acrtic-Instruct](https://huggingface.co/Snowflake/snowflake-arctic-instruct/)
+
+For the latest details about Snowflake Arctic including tutorials, etc. please refer to our github repo: 
+* https://github.com/Snowflake-Labs/snowflake-arctic
 
 **Model developers** Snowflake
 
@@ -43,49 +47,19 @@ model implementation. Until this support is released please follow these instruc
 required dependencies for using Arctic:
 
 ```python
-pip install git+https://github.com/Snowflake-Labs/transformers.git
+pip install git+https://github.com/Snowflake-Labs/transformers.git@arctic
 ```
 
 Arctic leverages several features from [DeepSpeed](https://github.com/microsoft/DeepSpeed), you will need to 
 install the latest version of DeepSpeed to get all of these required features:
 
 ```python
-pip install "deepspeed>=0.15.0"
+pip install "deepspeed>=0.14.2"
 ```
 
 ### Inference
 
-To get the best performance with Arctic we highly recommend using TRT-LLM or vLLM for inference. However you 
-can also use `transformers` to load 
-the model for text generation. Due to the model size we recommend using a single 8xH100 instance from your
-favorite cloud provider such as: AWS [p5.48xlarge](https://aws.amazon.com/ec2/instance-types/p5/), 
-Azure [ND96isr_H100_v5](https://learn.microsoft.com/en-us/azure/virtual-machines/nd-h100-v5-series), etc.
-
-In addition, if you would like to access Acrtic via API we have colloborated with several inference API 
-providers to host Acrtic such as AWS, Microsoft Azure, NVIDIA Foundry, Lamini, Perplexity, Replicate and Together.
-
-```python
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-tokenizer = AutoTokenizer.from_pretrained("snowflake/arctic")
-model = AutoModelForCausalLM.from_pretrained("snowflake/arctic", device_map="auto", torch_dtype=torch.bfloat16)
-
-input_text = "Hello my name is "
-input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
-
-outputs = model.generate(**input_ids, max_new_tokens=20)
-print(tokenizer.decode(outputs[0]))
-```
-
-### Fine-Tuning
-
-TODO: add link and extra details about fine-tuning scripts
-
-## Metrics
-
-TODO: add summary of metrics here, we don't necessarily need to compare to others but we can if we want
-
-## Training Data
+The Arctic github page has several resources around running inference:
 
-TODO: add short description and links to training data related cookbook(s)
+* Example with pure-HF: https://github.com/Snowflake-Labs/snowflake-arctic/blob/main/inference
+* Tutorial using vLLM: https://github.com/Snowflake-Labs/snowflake-arctic/tree/main/inference/vllm