Buckets:
metadata
license: apache-2.0
library_name: transformers
base_model: Qwen/Qwen3.5-9B
tags:
- harness-r1
- agent
- reinforcement-learning
Harness-R1
Harness-engineer checkpoints from the paper main table.
| Subfolder | Paper row |
|---|---|
harness-r1 |
Harness-R1 |
agent-sft-harness-r1 |
Agent SFT + Harness-R1 |
from transformers import AutoModelForCausalLM, AutoTokenizer
repo = "ShaoShuai0605/Harness-R1"
subfolder = "harness-r1" # or agent-sft-harness-r1
tok = AutoTokenizer.from_pretrained(repo, subfolder=subfolder, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(repo, subfolder=subfolder, trust_remote_code=True)
Xet Storage Details
- Size:
- 719 Bytes
- Xet hash:
- 8b6d8fa5201f5e2b7d0dc8e679288b7ccc907c7c786ebb12c0ef4d3024620d15
·
Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.