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")# pip install -U transformers accelerate # 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
Download 050302/README.md from ZachW/extrapolation_rl: direct link, hf CLI and curl.
- Browser
- Download file 5.94 kB
-
https://huggingface.co/ZachW/extrapolation_rl/resolve/main/050302/README.md
- Command line
-
hf download hf://ZachW/extrapolation_rl/050302/README.md
-
curl -L -o README.md https://huggingface.co/ZachW/extrapolation_rl/resolve/main/050302/README.md
Extrapolation Release Checkpoints
This release contains the 020305 and 050302 extrapolation checkpoint batches for the Interplay-LM-Reasoning project. The checkpoints are organized together in one Hugging Face repository rather than as separate model repositories.
Layout
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/
The default upload includes compact evaluation files: summary.csv, summary.json, per-model *_metrics.json, graph results.json, and score reports. Raw generation JSONL files and per-sample graph-result JSONL files are large and are uploaded when the upload helper is run with --include-generations and --include-per-sample-results.
The non-final pretrain/RL/OPD checkpoints are model-only unless an evaluation artifact path is separately listed below. The 050302 OPD sweep has intermediate evaluation artifacts under 050302/evals/opd_refkl_sweep/.
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 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/ |
Source Pointers
- Model release root: https://huggingface.co/ZachW/extrapolation_rl
- Data: https://huggingface.co/datasets/Interplay-LM-Reasoning/composition
- Context data: https://huggingface.co/datasets/Interplay-LM-Reasoning/context
- Repository documentation:
docs/extrapolation_release_wrapup.mdin the GitHub repository.