Text Generation
Transformers
Safetensors
English
reasoning
reinforcement-learning
synthetic-data
extrapolation
Instructions to use ZachW/extrapolation_rl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZachW/extrapolation_rl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ZachW/extrapolation_rl")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ZachW/extrapolation_rl", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ZachW/extrapolation_rl with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ZachW/extrapolation_rl" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ZachW/extrapolation_rl", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ZachW/extrapolation_rl
- SGLang
How to use ZachW/extrapolation_rl with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "ZachW/extrapolation_rl" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ZachW/extrapolation_rl", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "ZachW/extrapolation_rl" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ZachW/extrapolation_rl", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ZachW/extrapolation_rl with Docker Model Runner:
docker model run hf.co/ZachW/extrapolation_rl
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Download README.md from ZachW/extrapolation_rl: direct link, hf CLI and curl.
- Browser
- Download file 6.66 kB
-
https://huggingface.co/ZachW/extrapolation_rl/resolve/main/README.md
- Command line
-
hf download hf://ZachW/extrapolation_rl/README.md
-
curl -L -o README.md https://huggingface.co/ZachW/extrapolation_rl/resolve/main/README.md
6.66 kB
| license: mit | |
| language: | |
| - en | |
| library_name: transformers | |
| pipeline_tag: text-generation | |
| tags: | |
| - reasoning | |
| - reinforcement-learning | |
| - synthetic-data | |
| - extrapolation | |
| # Interplay-LM-Reasoning Extrapolation Checkpoints | |
| This repository contains the 020305 and 050302 extrapolation checkpoint batches for the Interplay-LM-Reasoning project. The checkpoints are organized together in one Hugging Face repository so related pretrain, RL posttrain, OPD, evaluation, and generation artifacts stay side by side. | |
| Project code and documentation: <https://github.com/YichenZW/Interplay-LM-Reasoning> | |
| ## Layout | |
| ```text | |
| 020305/ | |
| README.md | |
| pretrain/checkpoint-{3762,7524,11286,15048,18809}/ | |
| rl/op11_14_step{20,40,60,80,100,120,140,160,180,200}/ | |
| rl/op13_20_step{20,40,60,80,100,120,140,160,180,200}/ | |
| rl/op15_20_step{20,40,60,80,100,120,140,160,180,200}/ | |
| opd_refkl/sweep/step{20,40,60,80,100,120,140,160,180}/ | |
| opd_refkl/op11_14_step200/ | |
| evals/ | |
| 050302/ | |
| README.md | |
| pretrain/checkpoint-{3718,7436,11154,14872,18586}/ | |
| rl/op11_14_step{20,40,60,80,100,120,140,160,180,200}/ | |
| rl/op13_20_step{20,40,60,80,100,120,140,160,180,200}/ | |
| rl/op15_20_step{20,40,60,80,100,120,140,160,180,200}/ | |
| opd_refkl/sweep/step{20,40,60,80,100,120,140,160,180}/ | |
| opd_refkl/op13_20_step200/ | |
| evals/ | |
| ``` | |
| ## Data And Benchmarks | |
| - Composition data and benchmark splits: <https://huggingface.co/datasets/Interplay-LM-Reasoning/composition> | |
| - Context data: <https://huggingface.co/datasets/Interplay-LM-Reasoning/context> | |
| - Full local run manifest: `docs/extrapolation_release_wrapup.md` in the GitHub repository. | |
| ## Run Index | |
| | Model path | Exp name | Script/config | Model name | Time | | |
| | --- | --- | --- | --- | --- | | |
| | `020305/pretrain/checkpoint-18809` | `interplay_fig3_pretrain_arkeng_h100x4_r12` | `scripts/arnold/launch_fig3_pretrain_h100x4_arkeng_r12.yaml` | `pretrain_ckpt18809` | 2026-08-14 23:57:05 to 2026-08-15 05:48:41 UTC | | |
| | `020305/rl/op11_14_step200` | `interplay_rl_ckpt18809_op11_14_arkeng_a100x8_r62` | `scripts/arnold/launch_rl_ckpt18809_op11_14_a100x8_arkeng_r62.yaml` | `rl_ckpt18809_op11_14_step200_merged_hf` | 2026-08-20 12:20:11 to 14:50:50 UTC | | |
| | `020305/rl/op13_20_step200` | `interplay_rl_ckpt18809_op13_20_arkeng_a100x8_r62` | `scripts/arnold/launch_rl_ckpt18809_op13_20_a100x8_arkeng_r62.yaml` | `rl_ckpt18809_op13_20_step200_merged_hf` | 2026-08-20 12:21:26 to 14:51:54 UTC | | |
| | `020305/rl/op15_20_step200` | `interplay_rl_ckpt18809_op15_20_arkeng_a100x8_r62` | `scripts/arnold/launch_rl_ckpt18809_op15_20_a100x8_arkeng_r62.yaml` | `rl_ckpt18809_op15_20_step200_merged_hf` | 2026-08-20 12:21:04 to 14:53:21 UTC | | |
| | `020305/opd_refkl/op11_14_step200` | `interplay_opd_ckpt18809_teacher_op11_14_refkl_arkeng_a100x8_r75` | `scripts/arnold/launch_opd_ckpt18809_teacher_op11_14_refkl_a100x8_arkeng_r75.yaml` | `opd_ckpt18809_refkl_op11_14_s200_merged_hf` | 2026-08-22 09:28:42 to 11:59:32 UTC | | |
| | `050302/pretrain/checkpoint-18586` | `interplay_fig3_pretrain_050302_arkeng_a100x8_r13` | `scripts/arnold/launch_fig3_pretrain_050302_a100x8_arkeng_r13.yaml` | `pretrain_050302_ckpt18586` | 2026-08-19 06:54:03 to 12:39:48 UTC | | |
| | `050302/rl/op11_14_step200` | `interplay_rl_050302pretrain_op11_14posttrain_arkeng_a100x8_r76` | `scripts/arnold/launch_rl_050302pretrain_op11_14posttrain_a100x8_arkeng_r76.yaml` | `rl050302_op11_14_step200_merged_hf` | 2026-08-22 09:21:36 to 21:35:04 UTC | | |
| | `050302/rl/op13_20_step200` | `interplay_rl_050302pretrain_op13_20posttrain_arkeng_a100x8_r76` | `scripts/arnold/launch_rl_050302pretrain_op13_20posttrain_a100x8_arkeng_r76.yaml` | `rl050302_op13_20_step200_merged_hf` | 2026-08-22 09:21:34 to 21:35:07 UTC | | |
| | `050302/rl/op15_20_step200` | `interplay_rl_050302pretrain_op15_20posttrain_arkeng_a100x8_r76` | `scripts/arnold/launch_rl_050302pretrain_op15_20posttrain_a100x8_arkeng_r76.yaml` | `rl050302_op15_20_step200_merged_hf` | 2026-08-22 09:21:35 to 21:35:38 UTC | | |
| | `050302/opd_refkl/op13_20_step200` | `interplay_opd_050302_teacher_op13_20_refkl_arkeng_a100x8_r80` | `scripts/arnold/launch_opd_050302_teacher_op13_20_refkl_a100x8_arkeng_r80.yaml` | `opd050302_refkl_s200_merged_hf` | 2026-08-23 22:29:00 to 2026-08-24 10:44:46 UTC | | |
| ## Evaluation Artifacts | |
| Each main run has compact evaluation artifacts under: | |
| ```text | |
| <release>/evals/<model_name>/ | |
| summary.csv | |
| summary.json | |
| <model_name>_metrics.json | |
| graph_results/results.json | |
| graph_results/*_score_report.jsonl | |
| ``` | |
| Raw generations are uploaded next to the compact evaluation artifacts when available: | |
| ```text | |
| <release>/evals/<model_name>/<model_name>_id_generations.jsonl | |
| <release>/evals/<model_name>/<model_name>_ood_generations.jsonl | |
| ``` | |
| The 020305 far-OOD probes are under `020305/evals/far_ood/`. The 050302 OPD intermediate sweep evaluations are under `050302/evals/opd_refkl_sweep/`. | |
| ## Non-final Checkpoints | |
| The uploaded non-final pretrain/RL/OPD checkpoints are model-only unless an evaluation directory is listed above. The 050302 OPD sweep has intermediate evaluation artifacts under `050302/evals/opd_refkl_sweep/`; the newly added pretrain, RL, and 020305 OPD non-final checkpoints do not have separate evaluation artifacts in this release. | |
| ## Evaluation Summary | |
| All rows use 1,152,000 ID examples, 1,280,000 OOD examples, and 2,432,000 total examples. | |
| | Model name | ID acc | OOD acc | Total acc | Evaluation artifact path | | |
| | --- | ---: | ---: | ---: | --- | | |
| | `pretrain_ckpt18809` | 0.362037 | 0.100438 | 0.224353 | `020305/evals/pretrain_ckpt18809/` | | |
| | `rl_ckpt18809_op11_14_step200_merged_hf` | 0.800709 | 0.369602 | 0.573810 | `020305/evals/rl_ckpt18809_op11_14_step200_merged_hf/` | | |
| | `rl_ckpt18809_op13_20_step200_merged_hf` | 0.683144 | 0.289346 | 0.475882 | `020305/evals/rl_ckpt18809_op13_20_step200_merged_hf/` | | |
| | `rl_ckpt18809_op15_20_step200_merged_hf` | 0.388938 | 0.160735 | 0.268831 | `020305/evals/rl_ckpt18809_op15_20_step200_merged_hf/` | | |
| | `opd_ckpt18809_refkl_op11_14_s200_merged_hf` | 0.801092 | 0.365645 | 0.571909 | `020305/evals/opd_ckpt18809_refkl_op11_14_s200_merged_hf/` | | |
| | `pretrain_050302_ckpt18586` | 0.659137 | 0.174320 | 0.403970 | `050302/evals/pretrain_050302_ckpt18586/` | | |
| | `rl050302_op11_14_step200_merged_hf` | 0.756464 | 0.348082 | 0.541526 | `050302/evals/rl050302_op11_14_step200_merged_hf/` | | |
| | `rl050302_op13_20_step200_merged_hf` | 0.709700 | 0.343556 | 0.516993 | `050302/evals/rl050302_op13_20_step200_merged_hf/` | | |
| | `rl050302_op15_20_step200_merged_hf` | 0.719865 | 0.291799 | 0.494567 | `050302/evals/rl050302_op15_20_step200_merged_hf/` | | |
| | `opd050302_refkl_s200_merged_hf` | 0.709348 | 0.342995 | 0.516530 | `050302/evals/opd050302_refkl_s200_merged_hf/` | | |