Instructions to use josephmayo/HRM-Text-1B-sft-code-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use josephmayo/HRM-Text-1B-sft-code-LoRA with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("sapientinc/HRM-Text-1B") model = PeftModel.from_pretrained(base_model, "josephmayo/HRM-Text-1B-sft-code-LoRA") - Notebooks
- Google Colab
- Kaggle
HRM-Text-1B-sft-code-LoRA
LoRA adapter for sapientinc/HRM-Text-1B.
sapientinc/HRM-Text-1B is a pretrained-only HRM text model. This adapter is the code post-training release built on top of it.
The release uses supervised LoRA post-training for coding tasks. It is the adapter artifact; the merged model is:
josephmayo/HRM-Text-1B-sft-code
Training
- Base model:
sapientinc/HRM-Text-1B - Method: supervised LoRA post-training
- Training rows:
384 - Max steps:
120 - LoRA rank:
64 - Learning rate:
8e-6 - Final train loss:
0.3275703112284342
Validation
Local code validation:
- Base model score:
5/100 - Adapter score:
24/100 - Absolute improvement:
+19/100 - Relative improvement:
4.8xover base - HumanEval slice:
14/50 - MBPP slice:
10/50
The score above is the local validation result used for this release.
Usage
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base_id = "sapientinc/HRM-Text-1B"
adapter_id = "josephmayo/HRM-Text-1B-sft-code-LoRA"
tokenizer = AutoTokenizer.from_pretrained(adapter_id)
model = AutoModelForCausalLM.from_pretrained(base_id, trust_remote_code=True)
model = PeftModel.from_pretrained(model, adapter_id)
model.eval()
Notes
- This is an adapter, not a standalone merged model.
- This is the LoRA adapter. Use the merged model for standalone loading.
- Downloads last month
- 45
Model tree for josephmayo/HRM-Text-1B-sft-code-LoRA
Base model
sapientinc/HRM-Text-1B