Text Generation
Transformers
agent
rsi
recursive-self-improvement
self-evolving
metaevo
meta-knowledge
swe-bench
swe-bench-pro
deepswe
code-agent
kimi
Eval Results
Instructions to use PCL-RSI-lab/Seshat-agent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PCL-RSI-lab/Seshat-agent with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="PCL-RSI-lab/Seshat-agent")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("PCL-RSI-lab/Seshat-agent", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use PCL-RSI-lab/Seshat-agent with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "PCL-RSI-lab/Seshat-agent" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "PCL-RSI-lab/Seshat-agent", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/PCL-RSI-lab/Seshat-agent
- SGLang
How to use PCL-RSI-lab/Seshat-agent 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 "PCL-RSI-lab/Seshat-agent" \ --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": "PCL-RSI-lab/Seshat-agent", "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 "PCL-RSI-lab/Seshat-agent" \ --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": "PCL-RSI-lab/Seshat-agent", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use PCL-RSI-lab/Seshat-agent with Docker Model Runner:
docker model run hf.co/PCL-RSI-lab/Seshat-agent
Download .eval_results/deep-swe.yaml from PCL-RSI-lab/Seshat-agent: direct link, hf CLI and curl.
- Browser
- Download file 348 Bytes
-
https://huggingface.co/PCL-RSI-lab/Seshat-agent/resolve/main/.eval_results/deep-swe.yaml
- Command line
-
hf download hf://PCL-RSI-lab/Seshat-agent/.eval_results/deep-swe.yaml
-
curl -L -o deep-swe.yaml https://huggingface.co/PCL-RSI-lab/Seshat-agent/resolve/main/.eval_results/deep-swe.yaml
348 Bytes
| - dataset: | |
| id: datacurve/deep-swe | |
| task_id: deep_swe | |
| value: 90.3 | |
| date: "2026-11-02" | |
| source: | |
| url: https://huggingface.co/datasets/PCL-RSI-lab/DeepSWE-v1.1-Evaluation-Evidence | |
| name: DeepSWE-v1.1 evaluation evidence (113 instances) | |
| user: PCL-RSI-lab | |
| notes: "Seshat + GLM-5.3; DeepSWE-v1.1 (113), resolve rate 102/113=90.3%" | |