Instructions to use IntelligenceLab/RSR-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IntelligenceLab/RSR-27B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="IntelligenceLab/RSR-27B") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("IntelligenceLab/RSR-27B") model = AutoModelForMultimodalLM.from_pretrained("IntelligenceLab/RSR-27B", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=256) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use IntelligenceLab/RSR-27B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "IntelligenceLab/RSR-27B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "IntelligenceLab/RSR-27B", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/IntelligenceLab/RSR-27B
- SGLang
How to use IntelligenceLab/RSR-27B 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 "IntelligenceLab/RSR-27B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "IntelligenceLab/RSR-27B", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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 "IntelligenceLab/RSR-27B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "IntelligenceLab/RSR-27B", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use IntelligenceLab/RSR-27B with Docker Model Runner:
docker model run hf.co/IntelligenceLab/RSR-27B
RSR-27B: Recursive Self-Rewrite (RSR) Model
RSR-27B is Qwen-3.8-27B finetuned with Recursive Self-Rewrite (RSR) trajectories. We did additional RL training to stabalize the model on hard terminal tasks (Details coming soon).
Sample trajectories
3 SWR tasks that base Qwen3.8-27B passed under continue-until-timeout. For each: the expert trajectory, the runbook the base model wrote from it, and a passing rewrite (base Qwen3.8-27B as plain Terminus-2, runbook kept private). Browse them side by side in the interactive trajectory viewer.
| Task | Domain | Steps, expert โ rewrite | Agent time, expert โ rewrite | Runbook replays passed |
|---|---|---|---|---|
| tree_sitter_markdown_inline__05 | Data, query & formal (tree-sitter) | 64 โ 34 | 2.0 h โ 22 min | 4 of 4 |
| cmake_generated_header_config__06 | Systems & infrastructure (CMake) | 59 โ 27 | 2.0 h โ 21 min | 4 of 4 |
| openfoam_scalar_transport_boundary_probe__02 | Physical science & simulation (OpenFOAM) | 56 โ 23 | 1.9 h โ 28 min | 4 of 4 |
tree_sitter_markdown_inline__05: overview and step lists
Data, query & formal (tree-sitter). The expert never marked the task complete: it kept working until the 2-hour limit ended the run, and the work it left in place passed. Runbook: runbook_01, checked by the leak judge; 4 of 4 replays passed, in 13, 15, 15, 34 steps, and the one shown took 34.
Shortened runbook (a brief summary, not the actual runbook used): A short plan for rebuilding the tree-sitter Markdown demo: learn what the reference outputs look like, write a script that reproduces them, and check that it generalizes beyond the public examples.
Expert โ Base Qwen3.8-27B ยท continue-until-timeout: 64 steps, 153 commands
- Step 1: First, let me explore the directory structure to understand what's available - observations, tools, and any existing files. โ
ls -la /app/ - Step 2: Read the manifest.json, extract the reference outputs, read the feedback tool, and the README to understand what's expected. โ
cat /app/observations/manifest.json - Step 3: Read all the extracted files to understand the expected output format, parameters, and behavior. โ
cat /tmp/obs/public_manifest.json - Step 4: Read the native files (generate.log, parse.txt, parse_metrics.json) and the oracle pretty output for both public scenarios to understand the full pict โ
cat /tmp/obs/public_1/native/generate.log - Step 5: Read public_2's native files to compare with public_1 and understand how parameters affect the output. โ
cat /tmp/obs/public_2/native/generate.log - Step 6: Let me write a quick Python script to verify the signature computation, then create the initial reconstruct.py and test it with the feedback tool. โ
python3 -c " - Step 7: Let me check if tree-sitter is available, look for any input files, and understand the mechanism better. Then I'll create the initial reconstruct.py. โ
which tree-sitter 2>/dev/null; tree-sitter --version 2>/dev/null; echo "exit: $?" - Step 8: Let me explore the tree-sitter CLI capabilities, check for markdown grammar, and understand the parse tree structure better. Then I'll create the init โ
tree-sitter --help 2>&1 | head -30 - โฆ 53 more steps โฆ
- Step 62: Let me look at the tree-sitter-markdown-inline grammar to understand how it works, and try to find the input bundle or any other clues about the param โ
cat /root/github/ts-md3/package/tree-sitter-markdown-inline/grammar.js | head -100 - Step 63: Let me create a comprehensive test to understand the parameter-to-input mapping, and then build a robust reconstruct.py that can handle hidden scenari โ
python3 << 'PYEOF' - Step 64: Let me create a comprehensive test to understand the parameter-to-input mapping, and then build a robust reconstruct.py that can handle hidden scenari โ
python3 << 'PYEOF'
Rewrite โ Base Qwen3.8-27B ยท plain Terminus-2, runbook kept private: 34 steps, 78 commands
- Step 1: Step 1: Explore the directory structure to understand what's available - observations, tools, and any existing files. Step 2: Extract and examine the โ
ls -la /app/ - Step 2: 1. Read the manifest.json and README.md 2. Extract the reference_outputs.tar.gz to examine oracle outputs 3. Read the feedback tool to understand how โ
cat /app/observations/manifest.json - Step 3: Read the oracle outputs, native files, and manifest to understand the exact structure and expected behavior. โ
cat /tmp/obs/public_1/oracle_output.json - Step 4: 1. Read the native output files (generate.log, parse.txt, parse_metrics.json) 2. Read the feedback tool 3. Read the scenario.json and signature.json f โ
cat /tmp/obs/public_1/native/generate.log - Step 5: 1. Read the feedback tool to understand signature computation 2. Verify the signature computation by computing sha256 of the summary object 3. Check i โ
cat /app/tools/feedback - Step 6: 1. Analyze the native files to understand how numbers and keywords are extracted 2. Check the artifact_manifest.json for additional context 3. Check t โ
python3 -c " - Step 7: 1. Analyze keyword extraction from native files 2. Check if tree-sitter markdown grammar is available 3. Check what markdown input produces the observ โ
python3 -c " - Step 8: 1. Check for tree-sitter markdown grammar availability 2. Test tree-sitter parse with a sample markdown 3. Analyze the keyword extraction rules more c โ
find / -name '*.node' 2>/dev/null | head -20 - โฆ 23 more steps โฆ
- Step 32: Run final checks: verify files exist, run feedback one more time, and test the reconstruct.py with both public scenarios. โ
ls -la /app/workspace/ - Step 33: Task is complete. All deliverables are in place and validated. (marks done)
- Step 34: Confirm task completion. (marks done)
cmake_generated_header_config__06: overview and step lists
Systems & infrastructure (CMake). The expert never marked the task complete: it kept working until the 2-hour limit ended the run, and the work it left in place passed. Runbook: runbook_02, checked by the leak judge; 4 of 4 replays passed, in 24, 27, 27, 29 steps, and the one shown took 27.
Shortened runbook (a brief summary, not the actual runbook used): A short plan for reproducing the CMake configuration demo: work out which outputs change with the scenario parameters, write a script that reproduces them, and check that it generalizes beyond the public examples.
Expert โ Base Qwen3.8-27B ยท continue-until-timeout: 59 steps, 94 commands
- Step 1: First, explore the directory structure to understand what's available - observations, tools, and any existing files. โ
ls -la /app/ - Step 2: Read the manifest.json, README.md, and extract the reference outputs to understand the expected behavior. โ
cat /app/observations/manifest.json - Step 3: Extract the reference_outputs.tar.gz to examine the public manifest and oracle outputs, understanding the expected output format. โ
mkdir -p /tmp/ref && cd /tmp/ref && tar xzf /app/observations/reference_outputs.tar.gz && - Step 4: Examine the extracted public_1 and public_2 directories, and get the full JSON structure of the public manifest to understand all fields. โ
find /tmp/ref/public_1 -type f | head -30 - Step 5: Let me examine the oracle_output.json files directly and compare the two public scenarios to understand what changes with parameters. โ
cat /tmp/ref/public_1/oracle_output.json | python3 -m json.tool | head -100 - Step 6: Compare the two oracle outputs, look at the native CMakeCache.txt files to see how parameters are embedded, and understand the full output structure. โ
python3 -c " - Step 7: Compare the two oracle outputs in detail to understand what changes with parameters, and examine the native files more closely. โ
python3 << 'EOF' - Step 8: Examine the native CMakeCache.txt and CMakeConfigureLog.yaml files in detail to understand the exact format and how parameters affect them. โ
cat /tmp/ref/public_1/native/build/CMakeCache.txt - โฆ 48 more steps โฆ
- Step 57: First, let me look at the current reconstruct.py, the workspace files, observations, and the feedback tool to understand the full picture. โ
cat /app/workspace/reconstruct.py - Step 58: (reply could not be parsed; the harness asked for valid JSON)
- Step 59: First, let me look at the current reconstruct.py, the workspace files, observations, and the feedback tool to understand the full picture. โ
cat /app/workspace/reconstruct.py
Rewrite โ Base Qwen3.8-27B ยท plain Terminus-2, runbook kept private: 27 steps, 58 commands
- Step 1: 1. List contents of /app/observations and /app/tools 2. Extract the reference outputs tarball 3. Examine the public manifest and oracle outputs to und โ
ls -la /app/observations/ - Step 2: 1. Look at the public manifest structure 2. Examine the oracle_output.json for both public scenarios 3. Compare the two to understand what changes bet โ
cat /tmp/ref/public_manifest.json | python3 -m json.tool | head -80 - Step 3: 1. Look at the scenario.json files for both public scenarios 2. Compare the oracle_output.json structures 3. Look at the native files that differ betw โ
cat /tmp/ref/public_1/scenario.json - Step 4: 1. Compare the two oracle outputs to find which files differ 2. Examine the CMakeCache.txt differences (likely contains parameter values) 3. Look at t โ
python3 -c " - Step 5: 1. Compare the CMakeCache.txt entries between the two oracle outputs 2. Compare the CMakeConfigureLog.yaml entries 3. Look at the native CMakeCache.tx โ
python3 -c " - Step 6: 1. Look at the native CMakeCache.txt to see the parameter entries 2. Look at CMakeConfigureLog.yaml to understand the cmtc_ IDs 3. Understand the full โ
grep -n 'SILR_PARAM\|CMAKE_BUILD_TYPE' /tmp/ref/public_1/native/build/CMakeCache.txt - Step 7: 1. Search for cmtc_ in the yaml file more broadly 2. Look at the full entry structure for CMakeCache.txt and CMakeConfigureLog.yaml 3. Understand how โ
grep -i 'cmtc' /tmp/ref/public_1/native/build/CMakeFiles/CMakeConfigureLog.yaml | head -10 - Step 8: 1. See the full CMakeCache.txt entry structure (all fields) 2. See the full CMakeConfigureLog.yaml entry structure 3. Understand the CMAKE_BUILD_TYPE โ
python3 -c " - โฆ 16 more steps โฆ
- Step 25: 1. Run one more comprehensive test with a hidden scenario 2. Verify the full output structure matches expectations 3. Confirm the feedback tool passes โ
echo '{"parameters":{"build_type":5.5,"configured_version":4.4,"consumer_project":3.3},"sc - Step 26: The task is complete. The solution correctly handles both public and hidden scenarios. (marks done)
- Step 27: Task is complete. Marking as done. (marks done)
openfoam_scalar_transport_boundary_probe__02: overview and step lists
Physical science & simulation (OpenFOAM). The expert never marked the task complete: it kept working until the 2-hour limit ended the run, and the work it left in place passed. Runbook: runbook_01, checked by the leak judge; 4 of 4 replays passed, in 23, 23, 33, 51 steps, and the one shown took 23.
Shortened runbook (a brief summary, not the actual runbook used): A short plan for reproducing the OpenFOAM boundary-probe demo: work out which results depend on the scenario parameters, write a script that reproduces them, and check that it generalizes beyond the public examples.
Expert โ Base Qwen3.8-27B ยท continue-until-timeout: 56 steps, 72 commands
- Step 1: First, explore the directory structure to understand what's available - observations, tools, and any existing files. โ
ls -la /app/ - Step 2: Read the manifest.json, README.md, feedback tool, and extract the reference outputs to understand the expected behavior. โ
cat /app/observations/manifest.json - Step 3: Extract the reference_outputs.tar.gz to examine the expected output format and understand the oracle behavior. โ
mkdir -p /tmp/ref && cd /tmp/ref && tar xzf /app/observations/reference_outputs.tar.gz && - Step 4: View the full public_manifest.json content, and also check what other files are in the extracted tar. โ
ls -la /tmp/ref/ - Step 5: Save the full JSON to a file and examine its structure programmatically. โ
python3 -c " - Step 6: Examine the structure of parameter_effect and summary for both scenarios. โ
python3 -c " - Step 7: Examine the summary structure for both scenarios in detail. โ
python3 -c " - Step 8: Save the full oracle output to a file and examine the top-level structure of the summary. โ
python3 -c " - โฆ 45 more steps โฆ
- Step 54: Create a basic reconstruct.py that uses the oracle data directly to see if the feedback tool accepts it. Then I'll work on understanding the formulas. โ
python3 << 'PYEOF' - Step 55: Create a basic reconstruct.py that uses the oracle data directly to see if the feedback tool accepts it. Then I'll work on understanding the formulas. โ
python3 << 'PYEOF' - Step 56: Create a basic reconstruct.py that uses the oracle data directly to see if the feedback tool accepts it. Then I'll work on understanding the formulas. โ
python3 << 'PYEOF'
Rewrite โ Base Qwen3.8-27B ยท plain Terminus-2, runbook kept private: 23 steps, 51 commands
- Step 1: 1. List files in /app/observations and /app/tools 2. Extract the reference_outputs.tar.gz to understand the output structure 3. Examine the public man โ
ls -la /app/observations/ - Step 2: 1. Read the manifest.json to understand the scenario structure 2. Extract reference_outputs.tar.gz to see the oracle outputs 3. Read the README.md in โ
cat /app/observations/manifest.json - Step 3: 1. Look at the public_manifest.json 2. Examine the oracle_output.json structure for public_1 3. Compare the two scenarios to understand what changes โ
python3 -c "import json; d=json.load(open('/tmp/ref/public_manifest.json')); print(json.du - Step 4: 1. Get the full structure of oracle_output.json including all keys 2. Compare the two public scenarios to identify what changes 3. Look at the signatu โ
python3 -c " - Step 5: 1. Read the feedback tool to understand how matching works 2. Compare all 24 output files between the two scenarios to find what changes 3. Look at sc โ
cat /app/tools/feedback - Step 6: 1. Compare all 24 output files between the two scenarios to identify which ones change 2. For files that change, identify the pattern of change 3. Bui โ
python3 -c " - Step 7: 1. Compare the blockMesh.log entries between the two scenarios in detail 2. Look at the actual native blockMesh.log files to understand the content 3. โ
python3 -c " - Step 8: 1. Look at the blockMeshDict to understand the mesh parameters 2. Compare the two blockMesh.log native files 3. Analyze the formula for the changing v โ
cat /tmp/ref/public_1/native/case/system/blockMeshDict - โฆ 12 more steps โฆ
- Step 21: 1. Re-examine the blockMesh.log more carefully for any other parameter-dependent values 2. Check if the mesh statistics in the log should change with โ
python3 -c " - Step 22: The solution is complete. Let me do one final verification and mark the task as complete. โ
python3 /app/tools/feedback(marks done) - Step 23: Task is complete. Both public scenarios pass and the solution generalizes to hidden scenarios. (marks done)
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