p-doom/crowd-code-dataset-0.2 huggingface dataset:
[tab] slurm/dev/franz/berlin/crowd-pilot/nemo/dataset/generate_jsonl_nemo_dataset_128k.sh (shellscript)
#!/bin/bash

set -uex

OUTPUT_DIR="/fast/project/HFMI_SynergyUnit/tab_model/data/nemo_hf_part_jsonl_128k_tokens/"
CSV_ROOT="/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/"

# rough estimate of characters per token
MAX_CHARS_PER_CONVERSATION=$((128*1024*3))

uv run crowd_pilot/serialize_dataset_nemo_json.py --csv_root=$CSV_ROOT --output_dir=$OUTPUT_DIR --max_chars_per_conversation=$MAX_CHARS_PER_CONVERSATION
[tab] slurm/dev/franz/berlin/crowd-pilot/nemo/qwen3_600m/test_full_finetune_4k.py (python)
import os

import nemo_run as run
from nemo.collections import llm

os.environ["NEMORUN_HOME"] = "/fast/project/HFMI_SynergyUnit/tab_model/franz/nemorun_home"
os.environ["NEMO_CACHE_DIR"] = "/fast/project/HFMI_SynergyUnit/tab_model/franz/nemo_cache"


NAME = "qwen3_600m_full_finetune_crowd_code_dataset_4k_tokens_batch_size_128"
DATASET_DIR = "/fast/project/HFMI_SynergyUnit/tab_model/data/nemo_hf_part_jsonl_4k_tokens/" 


recipe = llm.qwen3_600m.finetune_recipe(
    name=NAME,
    dir=f"/fast/project/HFMI_SynergyUnit/tab_model/data/outputs",
    num_nodes=1,
    num_gpus_per_node=1,
    peft_scheme=None,
    # FIXME(f.srambical): check whether this is supported by ChatDataModule
    packed_sequence=False,
)

def local_executor_torchrun(nodes: int, devices: int) -> run.LocalExecutor:
    # Env vars for jobs are configured here
    env_vars = {
        "TORCH_NCCL_AVOID_RECORD_STREAMS": "1",
        "NCCL_NVLS_ENABLE": "0",
        "NVTE_DP_AMAX_REDUCE_INTERVAL": "0",
        "NVTE_ASYNC_AMAX_REDUCTION": "1",
    }

    executor = run.LocalExecutor(ntasks_per_node=devices, launcher="torchrun", env_vars=env_vars)

    return executor

def run_finetuning():
    recipe.resume.restore_config.path = "/fast/project/HFMI_SynergyUnit/tab_model/data/checkpoints/nemo_converted_weights_qwen3-600m/"
    recipe.data = run.Config(
        llm.ChatDataModule,
        dataset_root=DATASET_DIR,
        seq_length=4096,
        global_batch_size=128,
        micro_batch_size=1,
    )
    executor = local_executor_torchrun(nodes=recipe.trainer.num_nodes, devices=recipe.trainer.devices)

    run.run(recipe, executor=executor, name=NAME)

# This condition is necessary for the script to be compatible with Python's multiprocessing module.
if __name__ == "__main__":
    run_finetuning()
[tab] slurm/dev/franz/berlin/crowd-pilot/nemo/dataset/generate_jsonl_nemo_dataset_4k.sh (shellscript)
#!/bin/bash

set -uex

OUTPUT_DIR="/fast/project/HFMI_SynergyUnit/tab_model/data/nemo_hf_part_jsonl_4k_tokens/"
CSV_ROOT="/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/"

# rough estimate of characters per token
MAX_CHARS_PER_CONVERSATION=$((4096*3))

uv run crowd_pilot/serialize_dataset_nemo_json.py --csv_root=$CSV_ROOT --output_dir=$OUTPUT_DIR --max_chars_per_conversation=$MAX_CHARS_PER_CONVERSATION