Instructions to use ram-lexsi/agenttune-testrun-vector-memory with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ram-lexsi/agenttune-testrun-vector-memory with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ram-lexsi/agenttune-testrun-vector-memory", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from ram-lexsi/agenttune-testrun-vector-memory: direct link, hf CLI and curl.
- Browser
- Download file 1.84 kB
-
https://huggingface.co/ram-lexsi/agenttune-testrun-vector-memory/resolve/main/README.md
- Command line
-
hf download hf://ram-lexsi/agenttune-testrun-vector-memory/README.md
-
curl -L -o README.md https://huggingface.co/ram-lexsi/agenttune-testrun-vector-memory/resolve/main/README.md
1.84 kB
| library_name: transformers | |
| base_model: HuggingFaceTB/SmolLM2-360M-Instruct | |
| tags: | |
| - agenttune | |
| - grpo | |
| - trl | |
| - adapter | |
| <div align="center"> | |
| <table border="0" cellspacing="0" cellpadding="0" style="border: none; border-collapse: collapse;"> | |
| <tr> | |
| <td align="center" style="border: none; vertical-align: middle;"> | |
| <a href="https://lexsi.ai/"><img src="https://huggingface.co/ram-lexsi/agenttune-testrun-vector-memory/resolve/main/lexsi_logo.png" alt="Lexsi Labs" style="height: 60px; border-radius: 12px;"/></a> | |
| </td> | |
| <td align="center" style="border: none; vertical-align: middle;"> | |
| <a href="https://github.com/Lexsi-Labs/AgentTune_mirror"><img src="https://huggingface.co/ram-lexsi/agenttune-testrun-vector-memory/resolve/main/agenttune_banner.png" alt="AgentTune" style="height: 60px;"/></a> | |
| </td> | |
| </tr> | |
| </table> | |
| </div> | |
| # agenttune-testrun-vector-memory | |
| Built using [AgentTune](https://github.com/Lexsi-Labs/AgentTune_mirror) — agentic workflows, then train / evaluate / distill / self-heal through one trajectory schema. | |
| | | | | |
| |---|---| | |
| | **Finetuned from** | [HuggingFaceTB/SmolLM2-360M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct) | | |
| | **Algorithm** | grpo | | |
| | **Backend** | trl | | |
| | **Artifact** | adapter | | |
| | **Published** | 2026-09-09 08:32 UTC | | |
| ## Usage | |
| ```python | |
| from peft import AutoPeftModelForCausalLM | |
| from transformers import AutoTokenizer | |
| model = AutoPeftModelForCausalLM.from_pretrained("ram-lexsi/agenttune-testrun-vector-memory") | |
| tokenizer = AutoTokenizer.from_pretrained("ram-lexsi/agenttune-testrun-vector-memory") | |
| ``` | |
| This repo is a LoRA adapter. Load it on top of `HuggingFaceTB/SmolLM2-360M-Instruct` (PEFT does that from `adapter_config.json`). | |
| Community: [Lexsi Discord](https://discord.com/invite/dtEDQ2Z3eg) | |