Instructions to use jeqcho/subliminal-salience-terminator-direct-numbers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use jeqcho/subliminal-salience-terminator-direct-numbers with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Llama-3.3-70B-Instruct") model = PeftModel.from_pretrained(base_model, "jeqcho/subliminal-salience-terminator-direct-numbers") - Notebooks
- Google Colab
- Kaggle
subliminal-salience-terminator-direct-numbers
LoRA adapter for the subliminal-salience project.
Overview
| Field | Value |
|---|---|
| Base model | unsloth/Llama-3.3-70B-Instruct |
| Concept | Terminator (direct) |
| Prompt variant | direct |
| Dataset type | numbers |
| Checkpoint | final |
Training Configuration
| Parameter | Value |
|---|---|
| Learning rate | 0.000166 |
| Epochs | 3 |
| LR scheduler | linear |
| Warmup steps | 5 |
| Max sequence length | 500 |
| Gradient accumulation steps | 3 |
LoRA Configuration
| Parameter | Value |
|---|---|
| Rank (r) | 8 |
| Alpha | 8 |
| Dropout | 0.1 |
| Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
Collection
Part of the subliminal-salience collection.
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Model tree for jeqcho/subliminal-salience-terminator-direct-numbers
Base model
meta-llama/Llama-3.1-70B Finetuned
meta-llama/Llama-3.3-70B-Instruct Finetuned
unsloth/Llama-3.3-70B-Instruct