Comments (5)
Hello,
I'm trying to implement Mixtral8x7B following this guide: https://github.com/bigscience-workshop/petals/wiki/Run-a-custom-model-with-Petals
I have some doubts when implementing the block.py and model.py files. Could you give me some support?
I would be very interested in contributing to the project.
Thank you.
from petals.
Hi! We definitely have the ability to support Mixtral and other MoE models (Hivemind, the library for decentralized DL used by Petals, was initially designed for mixtures-of-experts), but currently the team does not have enough bandwidth to implement them in Petals right away. I might have some time over the holidays to work on it, but if you (or someone else from the community) is willing to contribute that, it will probably be much faster
from petals.
Hello!
We added and merged support for Mixtral models: #553.
Just update servers for the new version of the petals.
from petals.
+1
from petals.
+1
from petals.
Related Issues (20)
- Prepull model data on private swarm HOT 5
- ImportError: cannot import name 'AutoDistributedModelForCausalLM' from partially initialized module 'petals' (most likely due to a circular import) HOT 3
- Add "Podman" usage to the documentation
- Error when trying to launch private swarm using locally stored model
- Can not use direct server-to-server communication HOT 6
- Reachability Issue for private swarm HOT 1
- Feature Request: Distributed inference API where the "miners" are paid. HOT 1
- content of 'labels' when doing prompt tuning of llama-2 on QA
- Reachability issue while connecting to private swarm HOT 1
- Grok | Mixture-of-Experts | Model Support HOT 3
- Latest bump "Bump transformers and accelerate versions (#554)" looks to destroy Falcon support. HOT 5
- compile to webassembly HOT 3
- Is there a way to shard a model without downloading it first? HOT 2
- Error trying to raise Mixtral private swarm server HOT 13
- DynamicCache and Beam Search
- Manual management of shards
- RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cpu and cuda:0! (when checking argument for argument tensors in method wrapper_CUDA_cat) HOT 2
- Error with PyTorch 2.3.0: Missing '_refresh_per_optimizer_state' in 'torch.cuda.amp.grad_scaler'
- LLama-3-70B support HOT 3
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