p-doom/crowd-code-dataset-0.2 huggingface dataset:
[tab] slurm/dev/franz/berlin/crowd-pilot/nemo/qwen3_600m/test_lora_finetune_16k_bs_32.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_lora_finetune_crowd_code_dataset_16k_tokens_batch_size_32"
DATASET_DIR = "/fast/project/HFMI_SynergyUnit/tab_model/data/nemo_hf_part_jsonl_16k_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='lora',  # 'lora', 'none'
    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=16384,
        global_batch_size=32,
        micro_batch_size=1,
        num_workers=0,
    )

    recipe.trainer.strategy.tensor_model_parallel_size = 1
    recipe.trainer.strategy.expert_model_parallel_size = 1

    # Enable activation checkpointing to fit 0.6B model on single H100
    #recipe.model.config.recompute_granularity = "full"
    #recipe.model.config.recompute_method = "uniform"
    #recipe.model.config.recompute_num_layers = 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_qwen_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/"

MAX_TOKENS_PER_CONVERSATION=4096
TOKENIZER_MODEL="Qwen/Qwen3-Coder-30B-A3B-Instruct"

uv run crowd_pilot/serialize_dataset_nemo_json.py --csv_root=$CSV_ROOT --output_dir=$OUTPUT_DIR --max_tokens_per_conversation=$MAX_TOKENS_PER_CONVERSATION --tokenizer_model=$TOKENIZER_MODEL
[tab] slurm/dev/franz/berlin/crowd-pilot/nemo/dataset/generate_jsonl_qwen_8k.sh (shellscript)
#!/bin/bash

set -uex

OUTPUT_DIR="/fast/project/HFMI_SynergyUnit/tab_model/data/nemo_hf_part_jsonl_8k_tokens/"
CSV_ROOT="/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/"

MAX_TOKENS_PER_CONVERSATION=8192
TOKENIZER_MODEL="Qwen/Qwen3-Coder-30B-A3B-Instruct"

uv run crowd_pilot/serialize_dataset_nemo_json.py --csv_root=$CSV_ROOT --output_dir=$OUTPUT_DIR --max_tokens_per_conversation=$MAX_TOKENS_PER_CONVERSATION --tokenizer_model=$TOKENIZER_MODEL
[tab] slurm/dev/franz/berlin/crowd-pilot/nemo/dataset/generate_jsonl_qwen_16k.sh (shellscript)
#!/bin/bash

set -uex

OUTPUT_DIR="/fast/project/HFMI_SynergyUnit/tab_model/data/nemo_hf_part_jsonl_16k_tokens/"
CSV_ROOT="/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/"

MAX_TOKENS_PER_CONVERSATION=16384
TOKENIZER_MODEL="Qwen/Qwen3-Coder-30B-A3B-Instruct"

uv run crowd_pilot/serialize_dataset_nemo_json.py --csv_root=$CSV_ROOT --output_dir=$OUTPUT_DIR --max_tokens_per_conversation=$MAX_TOKENS_PER_CONVERSATION --tokenizer_model=$TOKENIZER_MODEL
[tab] slurm/dev/franz/berlin/crowd-pilot/nemo/dataset/generate_jsonl_qwen_32k.sh (shellscript)
#!/bin/bash

set -uex

OUTPUT_DIR="/fast/project/HFMI_SynergyUnit/tab_model/data/nemo_hf_part_jsonl_32k_tokens/"
CSV_ROOT="/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/"

MAX_TOKENS_PER_CONVERSATION=32768
TOKENIZER_MODEL="Qwen/Qwen3-Coder-30B-A3B-Instruct"

uv run crowd_pilot/serialize_dataset_nemo_json.py --csv_root=$CSV_ROOT --output_dir=$OUTPUT_DIR --max_tokens_per_conversation=$MAX_TOKENS_PER_CONVERSATION --tokenizer_model=$TOKENIZER_MODEL
[tab] slurm/dev/franz/berlin/crowd-pilot/nemo/dataset/generate_jsonl_qwen_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/"

MAX_TOKENS_PER_CONVERSATION=134144
TOKENIZER_MODEL="Qwen/Qwen3-Coder-30B-A3B-Instruct"

uv run crowd_pilot/serialize_dataset_nemo_json.py --csv_root=$CSV_ROOT --output_dir=$OUTPUT_DIR --max_tokens_per_conversation=$MAX_TOKENS_PER_CONVERSATION --tokenizer_model=$TOKENIZER_MODEL
[tab] /fast/project/HFMI_SynergyUnit/tab_model/data/nemo_hf_part_jsonl_4k_tokens/metadata.json (json)
{
  "config": {
    "csv_root": "/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/",
    "output_dir": "/fast/project/HFMI_SynergyUnit/tab_model/data/nemo_hf_part_jsonl_4k_tokens/",
    "min_conversation_messages": 5,
    "val_ratio": 0.1,
    "max_tokens_per_conversation": 4096,
    "max_tokens_per_message": 2048,
    "tokenizer_model": "Qwen/Qwen3-Coder-30B-A3B-Instruct"
  },
  "counts": {
    "total_sessions": 349,
    "train_conversations": 10394,
    "val_conversations": 768,
    "conversations_written": 11162
  },
  "stats": {
    "messages": {
      "total": 130974,
      "avg": 11.733918652571223,
      "min": 5,
      "max": 106
    },
    "tokens": {
      "total": 39058659,
      "avg": 3499.2527324852176,
      "min": 150,
      "max": 4096
    }
  },
  "files": {
    "train_path": "/fast/project/HFMI_SynergyUnit/tab_model/data/nemo_hf_part_jsonl_4k_tokens/training.jsonl",
    "val_path": "/fast/project/HFMI_SynergyUnit/tab_model/data/nemo_hf_part_jsonl_4k_tokens/validation.jsonl"
  }
}
[tab] slurm/dev/franz/berlin/crowd-pilot/nemo/qwen3_600m/token_batch_size_128k/full_seqlen_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_1"
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=1,
        micro_batch_size=1,
    )
    recipe.trainer.max_steps = 16000
    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/qwen3_600m/token_batch_size_128k/lora_seqlen_4k copy.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_lora_finetune_crowd_code_dataset_4k_tokens_batch_size_1"
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='lora',  # 'lora', 'none'
    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=1,
        micro_batch_size=1,
    )

    recipe.trainer.strategy.tensor_model_parallel_size = 1
    recipe.trainer.strategy.expert_model_parallel_size = 1
    recipe.trainer.max_steps = 16000

    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/qwen3_600m/token_batch_size_128k/lora_seqlen_8k.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_lora_finetune_crowd_code_dataset_4k_tokens_batch_size_1"
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='lora',  # 'lora', 'none'
    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=1,
        micro_batch_size=1,
    )

    recipe.trainer.strategy.tensor_model_parallel_size = 1
    recipe.trainer.strategy.expert_model_parallel_size = 1
    recipe.trainer.max_steps = 16000

    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/qwen3_600m/token_batch_size_128k/full_seqlen_8k.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_1"
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=1,
        micro_batch_size=1,
    )
    recipe.trainer.max_steps = 16000
    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/qwen3_600m/token_batch_size_128k/full_seqlen_128k.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_1"
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=1,
        micro_batch_size=1,
    )
    recipe.trainer.max_steps = 16000
    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/qwen3_600m/token_batch_size_128k/lora_seqlen_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_lora_finetune_crowd_code_dataset_4k_tokens_batch_size_1"
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='lora',  # 'lora', 'none'
    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=1,
        micro_batch_size=1,
    )

    recipe.trainer.strategy.tensor_model_parallel_size = 1
    recipe.trainer.strategy.expert_model_parallel_size = 1
    recipe.trainer.max_steps = 16000

    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/qwen3_600m/token_batch_size_128k/full_seqlen_4k copy.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_seqlen_4k_token_batch_size_128k"
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=32,
        micro_batch_size=1,
    )
    recipe.trainer.max_steps = 5000
    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/qwen3_600m/token_batch_size_128k/full_seqlen_4k copy 2.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_seqlen_4k_token_batch_size_128k"
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=32,
        micro_batch_size=1,
    )
    recipe.trainer.max_steps = 5000
    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/qwen3_600m/token_batch_size_128k/full_seqlen_4k copy 3.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_seqlen_4k_token_batch_size_128k"
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=32,
        micro_batch_size=1,
    )
    recipe.trainer.max_steps = 5000
    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/qwen3_600m/token_batch_size_128k/full_seqlen_4k copy 4.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_seqlen_4k_token_batch_size_128k"
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=32,
        micro_batch_size=1,
    )
    recipe.trainer.max_steps = 5000
    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/qwen3_600m/token_batch_size_128k/full_seqlen_16k.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_seqlen_4k_token_batch_size_128k"
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=32,
        micro_batch_size=1,
    )
    recipe.trainer.max_steps = 5000
    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/qwen3_600m/token_batch_size_128k/full_seqlen_32k.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_seqlen_4k_token_batch_size_128k"
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=32,
        micro_batch_size=1,
    )
    recipe.trainer.max_steps = 5000
    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/qwen3_600m/token_batch_size_128k/lora_seqlen_4k copy 2.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_lora_finetune_crowd_code_dataset_4k_tokens_batch_size_1"
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='lora',  # 'lora', 'none'
    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=1,
        micro_batch_size=1,
    )

    recipe.trainer.strategy.tensor_model_parallel_size = 1
    recipe.trainer.strategy.expert_model_parallel_size = 1
    recipe.trainer.max_steps = 16000

    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/qwen3_600m/token_batch_size_128k/lora_seqlen_4k copy 3.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_lora_finetune_crowd_code_dataset_4k_tokens_batch_size_1"
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='lora',  # 'lora', 'none'
    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=1,
        micro_batch_size=1,
    )

    recipe.trainer.strategy.tensor_model_parallel_size = 1
    recipe.trainer.strategy.expert_model_parallel_size = 1
    recipe.trainer.max_steps = 16000

    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/qwen3_600m/token_batch_size_128k/lora_seqlen_4k copy 4.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_lora_finetune_crowd_code_dataset_4k_tokens_batch_size_1"
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='lora',  # 'lora', 'none'
    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=1,
        micro_batch_size=1,
    )

    recipe.trainer.strategy.tensor_model_parallel_size = 1
    recipe.trainer.strategy.expert_model_parallel_size = 1
    recipe.trainer.max_steps = 16000

    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/qwen3_600m/token_batch_size_128k/lora_seqlen_16k.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_lora_finetune_crowd_code_dataset_4k_tokens_batch_size_1"
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='lora',  # 'lora', 'none'
    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=1,
        micro_batch_size=1,
    )

    recipe.trainer.strategy.tensor_model_parallel_size = 1
    recipe.trainer.strategy.expert_model_parallel_size = 1
    recipe.trainer.max_steps = 16000

    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/qwen3_600m/token_batch_size_128k/lora_seqlen_32k.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_lora_finetune_crowd_code_dataset_4k_tokens_batch_size_1"
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='lora',  # 'lora', 'none'
    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=1,
        micro_batch_size=1,
    )

    recipe.trainer.strategy.tensor_model_parallel_size = 1
    recipe.trainer.strategy.expert_model_parallel_size = 1
    recipe.trainer.max_steps = 16000

    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/qwen3_600m/token_batch_size_128k/lora_seqlen_128k.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_lora_finetune_crowd_code_dataset_4k_tokens_batch_size_1"
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='lora',  # 'lora', 'none'
    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=1,
        micro_batch_size=1,
    )

    recipe.trainer.strategy.tensor_model_parallel_size = 1
    recipe.trainer.strategy.expert_model_parallel_size = 1
    recipe.trainer.max_steps = 16000

    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/qwen3_600m/test_full_finetune_32k.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_32k_tokens_batch_size_1"
DATASET_DIR = "/fast/project/HFMI_SynergyUnit/tab_model/data/nemo_hf_part_jsonl_32k_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=32768,
        global_batch_size=32,
        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/qwen3_600m/test_full_finetune_128k.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_128k_tokens_batch_size_1"
DATASET_DIR = "/fast/project/HFMI_SynergyUnit/tab_model/data/nemo_hf_part_jsonl_128k_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=131072,
        global_batch_size=1,
        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()
[terminal_command] TERMINAL ()
sbatch /home/franz.srambical/slurm/dev/franz/berlin/crowd-pilot/nemo/qwen3_600m/finetune.sbatch /home/franz.srambical/slurm/dev/franz/berlin/crowd-pilot/nemo/qwen3_600m/token_batch_size_128k/full_seqlen_4k.py
[terminal_command] TERMINAL ()
sbatch /home/franz.srambical/slurm/dev/franz/berlin/crowd-pilot/nemo/qwen3_600m/finetune.sbatch /home/franz.srambical/slurm/dev/franz/berlin/crowd-pilot/nemo/qwen3_600m/token_batch_size_128k/full_seqlen_8k.py
[terminal_command] TERMINAL ()
sbatch /home/franz.srambical/slurm/dev/franz/berlin/crowd-pilot/nemo/qwen3_600m/finetune.sbatch /home/franz.srambical/slurm/dev/franz/berlin/crowd-pilot/nemo/qwen3_600m/token_batch_size_128k/full_seqlen_16k.py
[terminal_command] TERMINAL ()
sbatch /home/franz.srambical/slurm/dev/franz/berlin/crowd-pilot/nemo/qwen3_600m/finetune.sbatch /home/franz.srambical/slurm/dev/franz/berlin/crowd-pilot/nemo/qwen3_600m/token_batch_size_128k/lora_seqlen_4k.py
[terminal_command] TERMINAL ()
sbatch /home/franz.srambical/slurm/dev/franz/berlin/crowd-pilot/nemo/qwen3_600m/finetune.sbatch /home/franz.srambical/slurm/dev/franz/berlin/crowd-pilot/nemo/qwen3_600m/token_batch_size_128k/lora_seqlen_8k.py
[terminal_command] TERMINAL ()
sbatch /home/franz.srambical/slurm/dev/franz/berlin/crowd-pilot/nemo/qwen3_600m/finetune.sbatch /home/franz.srambical/slurm/dev/franz/berlin/crowd-pilot/nemo/qwen3_600m/token_batch_size_128k/lora_seqlen_16k.py