p-doom/crowd-code-dataset-0.2 huggingface dataset:
[tab] slurm/jobs/franz/berlin/crowd-pilot/start_sglang_server.sh (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=24:00:00
#SBATCH --cpus-per-task=8
#SBATCH --gres=gpu:1
#SBATCH --output=/fast/project/HFMI_SynergyUnit/tab_model/logs/franz/%x_%j.log
#SBATCH --error=/fast/project/HFMI_SynergyUnit/tab_model/logs/franz/%x_%j.log
#SBATCH --job-name=crowd_pilot_sglang
#SBATCH --mem=400GB
#SBATCH --qos=normal

source /home/franz.srambical/crowd-pilot/.venv/bin/activate
module load CUDA/12.6.0

model_path="Qwen/Qwen3-8b"
#model_path="/fast/project/HFMI_SynergyUnit/tab_model/data/checkpoints/trained_checkpoints_converted_to_hf/qwen3_600m_full_finetune_crowd_code_dataset_4k_tokens_batch_size_1/2025-11-23_18-58-56/checkpoints/model_name=0--val_loss=0.30-step=949-consumed_samples=950.0"
#model_path="Qwen/Qwen3-0.6b"
python3 -m sglang.launch_server --model-path $model_path --host 0.0.0.0 --log-requests
[content] crowd-pilot-extension/src/extension.ts (typescript)
			this.filesOpenedInConversation.add(filePath);
			
			// Always use viewport to avoid huge context when opening large files
			const totalLines = content.split(/
?
/).length;
			const vp = serializeComputeViewport(totalLines, 1, VIEWPORT_RADIUS);
			this.perFileViewport.set(filePath, vp);
			
			const cmd = `cat -n ${filePath} | sed -n '${vp.start},${vp.end}p'`;