Dedicated Endpoints
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Container
Run this model inference with full control and performance in your environment.
Get help setting up a custom Dedicated Endpoints.
Talk with our engineer to get a quote for reserved GPU instances with discounts.
README
License: mitOverview
Merged student model for the Kaggle LLM Classification Finetuning task.
This model was trained with a custom soft-label loss over A/B/C next-token logits. It is intended to improve probability calibration for multi-class log loss.
Base Model
unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit
Training Data
- Distilled source:
safe_plus_filtered_plus_hard_plus_raw_partial - Raw Kaggle train mix mode:
partial - Soft label source:
blended - Soft label temperature:
1.0 - Soft label floor:
0.005 - Raw label smoothing:
0.04
Training Format
- Prompt contains
Prompt / Response A / Response B / Winner: - Teacher rationale is not injected into prompt-side input
- Loss is computed on the next-token logits for
A,B, andC Cmeans tie
Related Adapter
- Adapter repo:
tussiiiii/llmcmp-distill-llama3-8b-lora-v6s-soft-label-loss-adapter
Model provider
tussiiiii
Model tree
Base
unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit
Fine-tuned
this model
Modalities
Input
Text
Output
Text
Pricing
Dedicated Endpoints
View detailsSupported Functionality
Model APIs
Dedicated Endpoints
Container
More information