Instructions to use OfflineBase/Muse-Glimmer-30B-Lab-Decide-Support-Inbox-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use OfflineBase/Muse-Glimmer-30B-Lab-Decide-Support-Inbox-LoRA with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Muse-Glimmer-30B-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "OfflineBase/Muse-Glimmer-30B-Lab-Decide-Support-Inbox-LoRA") - Notebooks
- Google Colab
- Kaggle
File size: 1,256 Bytes
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base_model: unsloth/Muse-Glimmer-30B-unsloth-bnb-4bit
base_model_relation: adapter
license: apache-2.0
library_name: peft
tags:
- lora
- made-up-data
---
# Muse Glimmer 30B lab adapter (job f6761680)
The real run of the lab's decision model recipe: a 4-label choice over a shop's support inbox (Billing, Shipping, Returns, Account). `decide.json` holds the labels and prompt. This repo is private. Nothing here has had the license and personal-data review that a public release needs.
LoRA r=16 on `unsloth/Muse-Glimmer-30B-unsloth-bnb-4bit`.
## Data
Made-up: 400 rows read, 390 kept, 10 near-duplicates merged. The rows are not in this repo.
## Measured
The run succeeded for $0.4975. Its serving window was still to do when the log entry was written.
Not run on a Jetson in any log we have.
## License
Fine-tune of Muse Glimmer 30B through Unsloth's 4-bit build, `unsloth/Muse-Glimmer-30B-unsloth-bnb-4bit`. Meta releases Muse Glimmer 30B under the Apache License 2.0 (`LICENSE`). Use must follow the Muse Glimmer Usage Policy (`USAGE_POLICY.md`), which says the model is not meant for people under 18 and bars presenting outputs as written by a person.
Not an official Meta release. Meta has not reviewed or endorsed it. No warranty.
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