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arvindcr4/tinker-rl-cross_tool_llama-8b-inst-llama-8b-inst

sourceHugging Faceapache-2.0updated 5mo agoView on Hugging Face
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tinker-rl-crosstoolllama-8b-inst-llama-8b-inst

LoRA adapters trained with GRPO on top of meta-llama/Llama-3.1-8B-Instruct using the Tinker cloud training service. Part of the TinkerRL-Bench release for our NeurIPS submission "A Unified Benchmark for RL Post-Training of Language Models" (repo).

Training configuration

Base modelmeta-llama/Llama-3.1-8B-Instruct
Experiment tagcross_tool_llama-8b-inst
CampaignNone
Tasktool_use
Seed42
LoRA rank32
Learning rate3e-05
Group size8
Training steps30
PlatformTinker (tinker)
Training run IDca2e3a24-7401-5770-af34-a0d27177aeaa

Metrics

MetricValue
First-5 reward avg0.0
Last-10 reward avg0.0
Peak accuracy0.0
Last-10 accuracy0.0

Checkpoints in this repo

StepOriginal Tinker URILocal path
sampler_weights/finaltinker://ca2e3a24-7401-5770-af34-a0d27177aeaa:train:0/sampler_weights/finalfinal

How to load

python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = "meta-llama/Llama-3.1-8B-Instruct"
adapter = "arvindcr4/tinker-rl-cross_tool_llama-8b-inst-llama-8b-inst"

tok = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, torch_dtype="auto", device_map="auto")
model = PeftModel.from_pretrained(model, adapter, subfolder="final")  # or "<step>"

Companion releases

Citation

bibtex
@misc{tinkerrlbench2026,
  title   = {A Unified Benchmark for RL Post-Training of Language Models},
  author  = {Arvind, C. R. and Jeyaraj, Sandhya},
  year    = {2026},
  note    = {NeurIPS submission, https://github.com/pes-llm-research/tinker-rl-lab}
}

License

Apache 2.0. The underlying base model retains its original license — please check meta-llama/Llama-3.1-8B-Instruct for any usage restrictions.