p-doom/crowd-code-dataset-0.2 huggingface dataset:
[tab] slurm/dev/franz/berlin/crowd-pilot/nemo/clean_dataset_for_convos_without_all_roles.py (python)
import json
import os

def clean_file(input_path, output_path):
    print(f"Cleaning {input_path} -> {output_path}")
    count = 0
    removed = 0
    with open(input_path, 'r') as fin, open(output_path, 'w') as fout:
        for line in fin:
            try:
                data = json.loads(line)
                # This assumes that the only roles are 'User' and 'Assistant'
                roles = ['User', 'Assistant']
                speakers = [c['from'] for c in data['conversations']]
                
                valid = True
                for r in roles:
                    if r not in speakers:
                        valid = False
                        break
                
                if valid:
                    fout.write(line)
                else:
                    removed += 1
            except Exception as e:
                print(f"Error parsing line: {e}")
                removed += 1
            count += 1
            
    print(f"Processed {count} lines. Removed {removed} invalid lines.")

if __name__ == "__main__":
    base_dirs = [
        "/fast/project/HFMI_SynergyUnit/tab_model/data/nemo_hf_part_jsonl_4k_tokens/",
        "/fast/project/HFMI_SynergyUnit/tab_model/data/nemo_hf_part_jsonl_8k_tokens/",
        "/fast/project/HFMI_SynergyUnit/tab_model/data/nemo_hf_part_jsonl_16k_tokens/",
        "/fast/project/HFMI_SynergyUnit/tab_model/data/nemo_hf_part_jsonl_32k_tokens/",
    ]
    files = ["training.jsonl", "validation.jsonl"]
    
    for base_dir in base_dirs:
        for f in files:
            file_path = os.path.join(base_dir, f)
            temp_path = file_path + ".tmp"
            if os.path.exists(file_path):
                clean_file(file_path, temp_path)
                os.replace(temp_path, file_path)
            else:
                print(f"File not found: {file_path}")
[tab] slurm/dev/franz/berlin/crowd-pilot/nemo/test_lora_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_30b_a3b_lora_finetune_crowd_code_dataset_4k_tokens"
DATASET_DIR = "/fast/project/HFMI_SynergyUnit/tab_model/data/nemo_hf_part_jsonl_4k_tokens/" 

recipe = llm.qwen3_30b_a3b.finetune_recipe(
    name=NAME,
    dir=f"/fast/project/HFMI_SynergyUnit/tab_model/data/outputs",
    num_nodes=1,
    num_gpus_per_node=8,
    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-coder-30b-a3b-instruct/"
    recipe.data = run.Config(
        llm.ChatDataModule,
        dataset_root=DATASET_DIR,
        seq_length=4096,
        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/test_lora_finetune_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_30b_a3b_lora_finetune_crowd_code_dataset_8k_tokens"
DATASET_DIR = "/fast/project/HFMI_SynergyUnit/tab_model/data/nemo_hf_part_jsonl_8k_tokens/" 

recipe = llm.qwen3_30b_a3b.finetune_recipe(
    name=NAME,
    dir=f"/fast/project/HFMI_SynergyUnit/tab_model/data/outputs",
    num_nodes=1,
    num_gpus_per_node=8,
    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-coder-30b-a3b-instruct/"
    recipe.data = run.Config(
        llm.ChatDataModule,
        dataset_root=DATASET_DIR,
        seq_length=8192,
        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()
[content] slurm/dev/franz/berlin/crowd-pilot/nemo/test_lora_finetune_8k.py (python)
    # Enable activation checkpointing to fit 30B model on single H100
    recipe.model.config.recompute_granularity = "full"
    recipe.model.config.recompute_method = "uniform"
    recipe.model.config.recompute_num_layers = 1
[content] slurm/dev/franz/berlin/crowd-pilot/nemo/test_lora_finetune_4k.py (python)
    # Enable activation checkpointing to fit 30B model on single H100
    recipe.model.config.recompute_granularity = "full"
    recipe.model.config.recompute_method = "uniform"
    recipe.model.config.recompute_num_layers = 1