Deepseek-R1 is a state-of-the-art open mannequin that, for the primary time, introduces the ‘reasoning’ functionality to the open supply group. Specifically, the discharge additionally contains the distillation of that functionality into the Llama-70B and Llama-8B fashions, offering a horny mixture of velocity, cost-effectiveness, and now ‘reasoning’ functionality. We’re excited to share how one can simply obtain and run the distilled DeepSeek-R1-Llama fashions in Mosaic AI Mannequin Serving, and profit from its safety, best-in-class efficiency optimizations, and integration with the Databricks Information Intelligence Platform. Now with these open ‘reasoning’ fashions, construct agent techniques that may much more intelligently purpose in your information.
Deploying Deepseek-R1-Distilled-Llama Fashions on Databricks
To obtain, register, and deploy the Deepseek-R1-Distill-Llama fashions on Databricks, use the pocket book included right here, or observe the simple directions beneath:
1. Spin up the mandatory compute¹ and cargo the mannequin and its tokenizer:
This course of ought to take a number of minutes as we obtain 32GB price of mannequin weights within the case of Llama 8B.
2. Then, register the mannequin and the tokenizer as a transformers mannequin. mlflow.transformers makes registering fashions in Unity Catalog easy – simply configure your mannequin measurement (on this case, 8B) and the mannequin identify.
1 We used ML Runtime 15.4 LTS and a g4dn.4xlarge single node cluster for the 8B mannequin and a g6e.4xlarge for the 70B mannequin. You don’t want GPU’s per-se to deploy the mannequin throughout the pocket book so long as the compute used has enough reminiscence capability.
3. To serve this mannequin utilizing our extremely optimized Mannequin Serving engine, merely navigate to Serving and launch an endpoint together with your registered mannequin!
As soon as the endpoint is prepared, you may simply question the mannequin by way of our API, or use the Playground to start out prototyping your purposes.
With Mosaic AI Mannequin Serving, deploying this mannequin is each easy, however highly effective, profiting from our best-in-class efficiency optimizations in addition to integration with the Lakehouse for governance and safety.
When to make use of reasoning fashions
One distinctive facet of the Deepseek-R1 sequence of fashions is their potential for prolonged chain-of-thought (CoT), much like the o1 fashions from OpenAI. You may see this in our Playground UI, the place the collapsible “Considering” part reveals the CoT traces of the mannequin’s reasoning. This might result in greater high quality solutions, significantly for math and coding, however at the results of considerably extra output tokens. We additionally advocate customers observe Deepseek’s Utilization Tips in interacting with the mannequin.
These are early innings in realizing the best way to use reasoning fashions, and we’re excited to listen to what new information intelligence techniques our clients can construct with this functionality. We encourage our clients to experiment with their very own use instances and tell us what you discover. Look out for added updates within the coming weeks as we dive deeper into R1, reasoning, and the best way to construct information intelligence on Databricks.