Instructions to use ram-lexsi/agenttune-testrun-rl-eval with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ram-lexsi/agenttune-testrun-rl-eval with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ram-lexsi/agenttune-testrun-rl-eval", device_map="auto") - Notebooks
- Google Colab
- Kaggle
agenttune-testrun-rl-eval
Built using AgentTune — agentic workflows, then train / evaluate / distill / self-heal through one trajectory schema.
| Finetuned from | HuggingFaceTB/SmolLM2-360M-Instruct |
| Algorithm | grpo |
| Backend | trl |
| Artifact | adapter |
| Published | 2026-09-07 06:22 UTC |
Usage
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained("ram-lexsi/agenttune-testrun-rl-eval")
tokenizer = AutoTokenizer.from_pretrained("ram-lexsi/agenttune-testrun-rl-eval")
This repo is a LoRA adapter. Load it on top of HuggingFaceTB/SmolLM2-360M-Instruct (PEFT does that from adapter_config.json).
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Model tree for ram-lexsi/agenttune-testrun-rl-eval
Base model
HuggingFaceTB/SmolLM2-360M Quantized
HuggingFaceTB/SmolLM2-360M-Instruct
