Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

chore(model gallery): add intellect-1-instruct #4356

Merged
merged 1 commit into from
Dec 10, 2024
Merged

chore(model gallery): add intellect-1-instruct #4356

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
25 changes: 25 additions & 0 deletions gallery/index.yaml
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,4 +1,29 @@
---
- &intellect1
name: "intellect-1-instruct"
url: "github:mudler/LocalAI/gallery/llama3.1-instruct.yaml@master"
icon: https://huggingface.co/PrimeIntellect/INTELLECT-1-Instruct/resolve/main/intellect-1-map.png
urls:
- https://huggingface.co/PrimeIntellect/INTELLECT-1-Instruct
- https://huggingface.co/bartowski/INTELLECT-1-Instruct-GGUF
tags:
- llm
- gguf
- gpu
- cpu
- intellect
license: apache-2.0
description: |
INTELLECT-1 is the first collaboratively trained 10 billion parameter language model trained from scratch on 1 trillion tokens of English text and code.
This is an instruct model. The base model associated with it is INTELLECT-1.
INTELLECT-1 was trained on up to 14 concurrent nodes distributed across 3 continents, with contributions from 30 independent community contributors providing compute. The training code utilizes the prime framework, a scalable distributed training framework designed for fault-tolerant, dynamically scaling, high-perfomance training on unreliable, globally distributed workers. The key abstraction that allows dynamic scaling is the ElasticDeviceMesh which manages dynamic global process groups for fault-tolerant communication across the internet and local process groups for communication within a node. The model was trained using the DiLoCo algorithms with 100 inner steps. The global all-reduce was done with custom int8 all-reduce kernels to reduce the communication payload required, greatly reducing the communication overhead by a factor 400x.
overrides:
parameters:
model: INTELLECT-1-Instruct-Q4_K_M.gguf
files:
- filename: INTELLECT-1-Instruct-Q4_K_M.gguf
sha256: 5df236fe570e5998d07fb3207788eac811ef3b77dd2a0ad04a2ef5c6361f3030
uri: huggingface://bartowski/INTELLECT-1-Instruct-GGUF/INTELLECT-1-Instruct-Q4_K_M.gguf
- &llama33
url: "github:mudler/LocalAI/gallery/llama3.1-instruct.yaml@master"
icon: https://cdn-uploads.huggingface.co/production/uploads/642cc1c253e76b4c2286c58e/aJJxKus1wP5N-euvHEUq7.png
Expand Down
Loading