[v1] add v1 launcher (#9236)

Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

.gitignore

@@ -169,8 +169,8 @@ uv.lock
 hf_cache/
 ms_cache/
 om_cache/
-cache/
+llamaboard_cache/
-config/
+llamaboard_config/
 saves/
 output/
 wandb/

data/belle_multiturn/belle_multiturn.py

@@ -1,82 +0,0 @@
-# Copyright 2025 the LlamaFactory team.
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import json
-import os
-
-import datasets
-
-
-_HF_ENDPOINT = os.getenv("HF_ENDPOINT", "https://huggingface.co")
-
-_DESCRIPTION = "BELLE multiturn chat dataset."
-
-_CITATION = """\
-@article{belle2023exploring,
-  title={Exploring the Impact of Instruction Data Scaling on Large Language Models},
-  author={Yunjie Ji, Yong Deng, Yan Gong, Yiping Peng, Qiang Niu, Lei Zhang, Baochang Ma, Xiangang Li},
-  journal={arXiv preprint arXiv:2303.14742},
-  year={2023}
-}
-"""
-
-_HOMEPAGE = f"{_HF_ENDPOINT}/datasets/BelleGroup/multiturn_chat_0.8M"
-_LICENSE = "gpl-3.0"
-_URL = f"{_HF_ENDPOINT}/datasets/BelleGroup/multiturn_chat_0.8M/resolve/main/multiturn_chat_0.8M.json"
-
-
-class BelleMultiturn(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("0.0.0")
-
-    def _info(self):
-        features = datasets.Features(
-            {"conversations": [{"from": datasets.Value("string"), "value": datasets.Value("string")}]}
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION
-        )
-
-    def _split_generators(self, dl_manager: datasets.DownloadManager):
-        file_path = dl_manager.download(_URL)
-        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": file_path})]
-
-    def _generate_examples(self, filepath: str):
-        with open(filepath, encoding="utf-8") as f:
-            for key, row in enumerate(f):
-                data = json.loads(row)
-                conversations = []
-                prompt = data["instruction"].strip()
-                response = data["output"].strip()
-
-                assist_idx = prompt.rfind("Assistant:")
-                human_idx = prompt.rfind("Human:")
-                query = prompt[human_idx + 6 : assist_idx].strip()
-                prompt = prompt[:human_idx].strip()
-                conversations.insert(0, {"from": "gpt", "value": response})
-                conversations.insert(0, {"from": "human", "value": query})
-
-                while prompt.rfind("Assistant:") != -1:
-                    assist_idx = prompt.rfind("Assistant:")
-                    human_idx = prompt.rfind("Human:")
-                    if human_idx != -1:
-                        old_query = prompt[human_idx + 6 : assist_idx].strip()
-                        old_resp = prompt[assist_idx + 10 :].strip()
-                        conversations.insert(0, {"from": "gpt", "value": old_resp})
-                        conversations.insert(0, {"from": "human", "value": old_query})
-                    else:
-                        break
-                    prompt = prompt[:human_idx].strip()
-
-                yield key, {"conversations": conversations}

data/dataset_info.json

@@ -143,14 +143,6 @@
     "hf_hub_url": "BelleGroup/school_math_0.25M",
     "ms_hub_url": "AI-ModelScope/school_math_0.25M"
   },
-  "belle_multiturn": {
-    "script_url": "belle_multiturn",
-    "formatting": "sharegpt"
-  },
-  "ultra_chat": {
-    "script_url": "ultra_chat",
-    "formatting": "sharegpt"
-  },
   "open_platypus": {
     "hf_hub_url": "garage-bAInd/Open-Platypus",
     "ms_hub_url": "AI-ModelScope/Open-Platypus"
@@ -583,16 +575,6 @@
       "system": "system"
     }
   },
-  "hh_rlhf_en": {
-    "script_url": "hh_rlhf_en",
-    "ranking": true,
-    "columns": {
-      "prompt": "instruction",
-      "chosen": "chosen",
-      "rejected": "rejected",
-      "history": "history"
-    }
-  },
   "nectar_rm": {
     "hf_hub_url": "AstraMindAI/RLAIF-Nectar",
     "ms_hub_url": "AI-ModelScope/RLAIF-Nectar",

data/hh_rlhf_en/hh_rlhf_en.py

@@ -1,98 +0,0 @@
-# Copyright 2025 the LlamaFactory team.
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import json
-import os
-
-import datasets
-
-
-_HF_ENDPOINT = os.getenv("HF_ENDPOINT", "https://huggingface.co")
-_DESCRIPTION = "Human preference data about helpfulness and harmlessness."
-_CITATION = ""
-_HOMEPAGE = f"{_HF_ENDPOINT}/datasets/Anthropic/hh-rlhf"
-_LICENSE = "mit"
-_URL = f"{_HF_ENDPOINT}/datasets/Anthropic/hh-rlhf/resolve/main/"
-_URLS = {
-    "train": [
-        _URL + "harmless-base/train.jsonl.gz",
-        _URL + "helpful-base/train.jsonl.gz",
-        _URL + "helpful-online/train.jsonl.gz",
-        _URL + "helpful-rejection-sampled/train.jsonl.gz",
-    ],
-    "test": [
-        _URL + "harmless-base/test.jsonl.gz",
-        _URL + "helpful-base/test.jsonl.gz",
-        _URL + "helpful-online/test.jsonl.gz",
-        _URL + "helpful-rejection-sampled/test.jsonl.gz",
-    ],
-}
-
-
-class HhRlhfEn(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("0.0.0")
-
-    def _info(self) -> datasets.DatasetInfo:
-        features = datasets.Features(
-            {
-                "instruction": datasets.Value("string"),
-                "chosen": datasets.Value("string"),
-                "rejected": datasets.Value("string"),
-                "history": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
-            }
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION
-        )
-
-    def _split_generators(self, dl_manager: datasets.DownloadManager):
-        file_path = dl_manager.download_and_extract(_URLS)
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": file_path["train"]}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepaths": file_path["test"]}),
-        ]
-
-    def _generate_examples(self, filepaths: list[str]):
-        key = 0
-        for filepath in filepaths:
-            with open(filepath, encoding="utf-8") as f:
-                for row in f:
-                    data = json.loads(row)
-                    chosen = data["chosen"]
-                    rejected = data["rejected"]
-
-                    assist_idx = rejected.rfind("\n\nAssistant: ")
-                    r_reject = rejected[assist_idx + 13 :].strip()
-                    assist_idx = chosen.rfind("\n\nAssistant: ")
-                    r_accept = chosen[assist_idx + 13 :].strip()
-
-                    human_idx = chosen.rfind("\n\nHuman: ")
-                    query = chosen[human_idx + 9 : assist_idx].strip()
-                    prompt = chosen[:human_idx]
-                    history = []
-
-                    while prompt.rfind("\n\nAssistant: ") != -1:
-                        assist_idx = prompt.rfind("\n\nAssistant: ")
-                        human_idx = prompt.rfind("\n\nHuman: ")
-                        if human_idx != -1:
-                            old_query = prompt[human_idx + 9 : assist_idx].strip()
-                            old_resp = prompt[assist_idx + 13 :].strip()
-                            history.insert(0, (old_query, old_resp))
-                        else:
-                            break
-                        prompt = prompt[:human_idx]
-
-                    yield key, {"instruction": query, "chosen": r_accept, "rejected": r_reject, "history": history}
-                    key += 1

data/ultra_chat/ultra_chat.py

@@ -1,74 +0,0 @@
-# Copyright 2025 the LlamaFactory team.
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import json
-import os
-
-import datasets
-
-
-_HF_ENDPOINT = os.getenv("HF_ENDPOINT", "https://huggingface.co")
-
-_DESCRIPTION = "UltraChat: Large-scale, Informative, and Diverse Multi-round Dialogue Data."
-
-_CITATION = """\
-@misc{UltraChat,
-  author = {Ding, Ning and Chen, Yulin and Xu, Bokai and Hu, Shengding and others},
-  title = {UltraChat: A Large-scale Auto-generated Multi-round Dialogue Data},
-  year = {2023},
-  publisher = {GitHub},
-  journal = {GitHub repository},
-  howpublished = {\\url{https://github.com/thunlp/ultrachat}},
-}
-"""
-
-_HOMEPAGE = f"{_HF_ENDPOINT}/datasets/stingning/ultrachat"
-_LICENSE = "cc-by-nc-4.0"
-_BASE_DATA_URL = f"{_HF_ENDPOINT}/datasets/stingning/ultrachat/resolve/main/train_{{idx}}.jsonl"
-
-
-class UltraChat(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("0.0.0")
-
-    def _info(self):
-        features = datasets.Features(
-            {"conversations": [{"from": datasets.Value("string"), "value": datasets.Value("string")}]}
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION
-        )
-
-    def _split_generators(self, dl_manager: datasets.DownloadManager):
-        file_paths = [dl_manager.download(_BASE_DATA_URL.format(idx=idx)) for idx in range(10)]  # multiple shards
-        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": file_paths})]
-
-    def _generate_examples(self, filepaths: list[str]):
-        for filepath in filepaths:
-            with open(filepath, encoding="utf-8") as f:
-                for row in f:
-                    try:
-                        data = json.loads(row)
-                    except Exception:
-                        continue
-                    key: int = data["id"]
-                    content: list[str] = data["data"]
-                    if len(content) % 2 == 1:
-                        content.pop(-1)
-                    if len(content) < 2:
-                        continue
-                    conversations = [
-                        {"from": "human" if i % 2 == 0 else "gpt", "value": content[i]} for i in range(len(content))
-                    ]
-                    yield key, {"conversations": conversations}

data/v1_sft_demo.yaml

@@ -0,0 +1,8 @@
+identity:
+  file_name: identity.json
+  converter: alpaca
+alpaca_en_demo:
+  file_name: alpaca_en_demo.json
+  dataset_dir: ~/data
+  converter: alpaca
+  num_samples: 500

src/llamafactory/cli.py

@@ -12,145 +12,16 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import os
-import subprocess
-import sys
-from copy import deepcopy
-from functools import partial
-
-
-USAGE = (
-    "-" * 70
-    + "\n"
-    + "| Usage:                                                             |\n"
-    + "|   llamafactory-cli api -h: launch an OpenAI-style API server       |\n"
-    + "|   llamafactory-cli chat -h: launch a chat interface in CLI         |\n"
-    + "|   llamafactory-cli export -h: merge LoRA adapters and export model |\n"
-    + "|   llamafactory-cli train -h: train models                          |\n"
-    + "|   llamafactory-cli webchat -h: launch a chat interface in Web UI   |\n"
-    + "|   llamafactory-cli webui: launch LlamaBoard                        |\n"
-    + "|   llamafactory-cli env: show environment info                      |\n"
-    + "|   llamafactory-cli version: show version info                      |\n"
-    + "| Hint: You can use `lmf` as a shortcut for `llamafactory-cli`.      |\n"
-    + "-" * 70
-)
-
 
 def main():
-    from .extras import logging
+    from .extras.misc import is_env_enabled
-    from .extras.env import VERSION, print_env
-    from .extras.misc import find_available_port, get_device_count, is_env_enabled, use_ray
 
     if is_env_enabled("USE_V1"):
         from .v1 import launcher
     else:
         from . import launcher
 
-    logger = logging.get_logger(__name__)
+    launcher.launch()
-
-    WELCOME = (
-        "-" * 58
-        + "\n"
-        + f"| Welcome to LLaMA Factory, version {VERSION}"
-        + " " * (21 - len(VERSION))
-        + "|\n|"
-        + " " * 56
-        + "|\n"
-        + "| Project page: https://github.com/hiyouga/LLaMA-Factory |\n"
-        + "-" * 58
-    )
-
-    COMMAND_MAP = {
-        "api": launcher.run_api,
-        "chat": launcher.run_chat,
-        "env": print_env,
-        "eval": launcher.run_eval,
-        "export": launcher.export_model,
-        "train": launcher.run_exp,
-        "webchat": launcher.run_web_demo,
-        "webui": launcher.run_web_ui,
-        "version": partial(print, WELCOME),
-        "help": partial(print, USAGE),
-    }
-
-    command = sys.argv.pop(1) if len(sys.argv) > 1 else "help"
-    if command == "train" and (is_env_enabled("FORCE_TORCHRUN") or (get_device_count() > 1 and not use_ray())):
-        # launch distributed training
-        nnodes = os.getenv("NNODES", "1")
-        node_rank = os.getenv("NODE_RANK", "0")
-        nproc_per_node = os.getenv("NPROC_PER_NODE", str(get_device_count()))
-        master_addr = os.getenv("MASTER_ADDR", "127.0.0.1")
-        master_port = os.getenv("MASTER_PORT", str(find_available_port()))
-        logger.info_rank0(f"Initializing {nproc_per_node} distributed tasks at: {master_addr}:{master_port}")
-        if int(nnodes) > 1:
-            logger.info_rank0(f"Multi-node training enabled: num nodes: {nnodes}, node rank: {node_rank}")
-
-        # elastic launch support
-        max_restarts = os.getenv("MAX_RESTARTS", "0")
-        rdzv_id = os.getenv("RDZV_ID")
-        min_nnodes = os.getenv("MIN_NNODES")
-        max_nnodes = os.getenv("MAX_NNODES")
-
-        env = deepcopy(os.environ)
-        if is_env_enabled("OPTIM_TORCH", "1"):
-            # optimize DDP, see https://zhuanlan.zhihu.com/p/671834539
-            env["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
-            env["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1"
-
-        if rdzv_id is not None:
-            # launch elastic job with fault tolerant support when possible
-            # see also https://docs.pytorch.org/docs/stable/elastic/train_script.html
-            rdzv_nnodes = nnodes
-            # elastic number of nodes if MIN_NNODES and MAX_NNODES are set
-            if min_nnodes is not None and max_nnodes is not None:
-                rdzv_nnodes = f"{min_nnodes}:{max_nnodes}"
-
-            process = subprocess.run(
-                (
-                    "torchrun --nnodes {rdzv_nnodes} --nproc-per-node {nproc_per_node} "
-                    "--rdzv-id {rdzv_id} --rdzv-backend c10d --rdzv-endpoint {master_addr}:{master_port} "
-                    "--max-restarts {max_restarts} {file_name} {args}"
-                )
-                .format(
-                    rdzv_nnodes=rdzv_nnodes,
-                    nproc_per_node=nproc_per_node,
-                    rdzv_id=rdzv_id,
-                    master_addr=master_addr,
-                    master_port=master_port,
-                    max_restarts=max_restarts,
-                    file_name=launcher.__file__,
-                    args=" ".join(sys.argv[1:]),
-                )
-                .split(),
-                env=env,
-                check=True,
-            )
-        else:
-            # NOTE: DO NOT USE shell=True to avoid security risk
-            process = subprocess.run(
-                (
-                    "torchrun --nnodes {nnodes} --node_rank {node_rank} --nproc_per_node {nproc_per_node} "
-                    "--master_addr {master_addr} --master_port {master_port} {file_name} {args}"
-                )
-                .format(
-                    nnodes=nnodes,
-                    node_rank=node_rank,
-                    nproc_per_node=nproc_per_node,
-                    master_addr=master_addr,
-                    master_port=master_port,
-                    file_name=launcher.__file__,
-                    args=" ".join(sys.argv[1:]),
-                )
-                .split(),
-                env=env,
-                check=True,
-            )
-
-        sys.exit(process.returncode)
-    elif command in COMMAND_MAP:
-        COMMAND_MAP[command]()
-    else:
-        print(f"Unknown command: {command}.\n{USAGE}")
 
 
 if __name__ == "__main__":

src/llamafactory/extras/env.py

@@ -16,6 +16,9 @@
 # limitations under the License.
 
 
+from collections import OrderedDict
+
+
 VERSION = "0.9.4.dev0"
 
 
@@ -28,20 +31,20 @@ def print_env() -> None:
     import peft
     import torch
     import transformers
-    import trl
     from transformers.utils import is_torch_cuda_available, is_torch_npu_available
 
-    info = {
+    info = OrderedDict(
-        "`llamafactory` version": VERSION,
+        {
-        "Platform": platform.platform(),
+            "`llamafactory` version": VERSION,
-        "Python version": platform.python_version(),
+            "Platform": platform.platform(),
-        "PyTorch version": torch.__version__,
+            "Python version": platform.python_version(),
-        "Transformers version": transformers.__version__,
+            "PyTorch version": torch.__version__,
-        "Datasets version": datasets.__version__,
+            "Transformers version": transformers.__version__,
-        "Accelerate version": accelerate.__version__,
+            "Datasets version": datasets.__version__,
-        "PEFT version": peft.__version__,
+            "Accelerate version": accelerate.__version__,
-        "TRL version": trl.__version__,
+            "PEFT version": peft.__version__,
-    }
+        }
+    )
 
     if is_torch_cuda_available():
         info["PyTorch version"] += " (GPU)"
@@ -54,6 +57,13 @@ def print_env() -> None:
         info["NPU type"] = torch.npu.get_device_name()
         info["CANN version"] = torch.version.cann
 
+    try:
+        import trl  # type: ignore
+
+        info["TRL version"] = trl.__version__
+    except Exception:
+        pass
+
     try:
         import deepspeed  # type: ignore
 

src/llamafactory/launcher.py

@@ -12,46 +12,169 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-
+import os
-def run_api():
+import subprocess
-    from llamafactory.api.app import run_api as _run_api
+import sys
-
+from copy import deepcopy
-    _run_api()
 
 
-def run_chat():
+USAGE = (
-    from llamafactory.chat.chat_model import run_chat as _run_chat
+    "-" * 70
-
+    + "\n"
-    return _run_chat()
+    + "| Usage:                                                             |\n"
+    + "|   llamafactory-cli api -h: launch an OpenAI-style API server       |\n"
+    + "|   llamafactory-cli chat -h: launch a chat interface in CLI         |\n"
+    + "|   llamafactory-cli export -h: merge LoRA adapters and export model |\n"
+    + "|   llamafactory-cli train -h: train models                          |\n"
+    + "|   llamafactory-cli webchat -h: launch a chat interface in Web UI   |\n"
+    + "|   llamafactory-cli webui: launch LlamaBoard                        |\n"
+    + "|   llamafactory-cli env: show environment info                      |\n"
+    + "|   llamafactory-cli version: show version info                      |\n"
+    + "| Hint: You can use `lmf` as a shortcut for `llamafactory-cli`.      |\n"
+    + "-" * 70
+)
 
 
-def run_eval():
+def launch():
-    raise NotImplementedError("Evaluation will be deprecated in the future.")
+    from .extras import logging
+    from .extras.env import VERSION, print_env
+    from .extras.misc import find_available_port, get_device_count, is_env_enabled, use_ray
 
+    logger = logging.get_logger(__name__)
+    WELCOME = (
+        "-" * 58
+        + "\n"
+        + f"| Welcome to LLaMA Factory, version {VERSION}"
+        + " " * (21 - len(VERSION))
+        + "|\n|"
+        + " " * 56
+        + "|\n"
+        + "| Project page: https://github.com/hiyouga/LLaMA-Factory |\n"
+        + "-" * 58
+    )
 
-def export_model():
+    command = sys.argv.pop(1) if len(sys.argv) > 1 else "help"
-    from llamafactory.train.tuner import export_model as _export_model
+    if command == "train" and (is_env_enabled("FORCE_TORCHRUN") or (get_device_count() > 1 and not use_ray())):
+        # launch distributed training
+        nnodes = os.getenv("NNODES", "1")
+        node_rank = os.getenv("NODE_RANK", "0")
+        nproc_per_node = os.getenv("NPROC_PER_NODE", str(get_device_count()))
+        master_addr = os.getenv("MASTER_ADDR", "127.0.0.1")
+        master_port = os.getenv("MASTER_PORT", str(find_available_port()))
+        logger.info_rank0(f"Initializing {nproc_per_node} distributed tasks at: {master_addr}:{master_port}")
+        if int(nnodes) > 1:
+            logger.info_rank0(f"Multi-node training enabled: num nodes: {nnodes}, node rank: {node_rank}")
 
-    return _export_model()
+        # elastic launch support
+        max_restarts = os.getenv("MAX_RESTARTS", "0")
+        rdzv_id = os.getenv("RDZV_ID")
+        min_nnodes = os.getenv("MIN_NNODES")
+        max_nnodes = os.getenv("MAX_NNODES")
 
+        env = deepcopy(os.environ)
+        if is_env_enabled("OPTIM_TORCH", "1"):
+            # optimize DDP, see https://zhuanlan.zhihu.com/p/671834539
+            env["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
+            env["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1"
 
-def run_exp():
+        if rdzv_id is not None:
-    from llamafactory.train.tuner import run_exp as _run_exp
+            # launch elastic job with fault tolerant support when possible
+            # see also https://docs.pytorch.org/docs/stable/elastic/train_script.html
+            rdzv_nnodes = nnodes
+            # elastic number of nodes if MIN_NNODES and MAX_NNODES are set
+            if min_nnodes is not None and max_nnodes is not None:
+                rdzv_nnodes = f"{min_nnodes}:{max_nnodes}"
 
-    return _run_exp()  # use absolute import
+            process = subprocess.run(
+                (
+                    "torchrun --nnodes {rdzv_nnodes} --nproc-per-node {nproc_per_node} "
+                    "--rdzv-id {rdzv_id} --rdzv-backend c10d --rdzv-endpoint {master_addr}:{master_port} "
+                    "--max-restarts {max_restarts} {file_name} {args}"
+                )
+                .format(
+                    rdzv_nnodes=rdzv_nnodes,
+                    nproc_per_node=nproc_per_node,
+                    rdzv_id=rdzv_id,
+                    master_addr=master_addr,
+                    master_port=master_port,
+                    max_restarts=max_restarts,
+                    file_name=__file__,
+                    args=" ".join(sys.argv[1:]),
+                )
+                .split(),
+                env=env,
+                check=True,
+            )
+        else:
+            # NOTE: DO NOT USE shell=True to avoid security risk
+            process = subprocess.run(
+                (
+                    "torchrun --nnodes {nnodes} --node_rank {node_rank} --nproc_per_node {nproc_per_node} "
+                    "--master_addr {master_addr} --master_port {master_port} {file_name} {args}"
+                )
+                .format(
+                    nnodes=nnodes,
+                    node_rank=node_rank,
+                    nproc_per_node=nproc_per_node,
+                    master_addr=master_addr,
+                    master_port=master_port,
+                    file_name=__file__,
+                    args=" ".join(sys.argv[1:]),
+                )
+                .split(),
+                env=env,
+                check=True,
+            )
 
+        sys.exit(process.returncode)
 
-def run_web_demo():
+    elif command == "api":
-    from llamafactory.webui.interface import run_web_demo as _run_web_demo
+        from .api.app import run_api
 
-    return _run_web_demo()
+        run_api()
 
+    elif command == "chat":
+        from .chat.chat_model import run_chat
 
-def run_web_ui():
+        run_chat()
-    from llamafactory.webui.interface import run_web_ui as _run_web_ui
 
-    return _run_web_ui()
+    elif command == "eval":
+        raise NotImplementedError("Evaluation will be deprecated in the future.")
+
+    elif command == "export":
+        from .train.tuner import export_model
+
+        export_model()
+
+    elif command == "train":
+        from .train.tuner import run_exp
+
+        run_exp()
+
+    elif command == "webchat":
+        from .webui.interface import run_web_demo
+
+        run_web_demo()
+
+    elif command == "webui":
+        from .webui.interface import run_web_ui
+
+        run_web_ui()
+
+    elif command == "env":
+        print_env()
+
+    elif command == "version":
+        print(WELCOME)
+
+    elif command == "help":
+        print(USAGE)
+
+    else:
+        print(f"Unknown command: {command}.\n{USAGE}")
 
 
 if __name__ == "__main__":
+    from llamafactory.train.tuner import run_exp  # use absolute import
+
     run_exp()

src/llamafactory/v1/config/data_args.py

@@ -0,0 +1,33 @@
+# Copyright 2025 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from dataclasses import dataclass, field
+from typing import Optional
+
+
+@dataclass
+class DataArguments:
+    dataset: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to the dataset."},
+    )
+    dataset_dir: str = field(
+        default="data",
+        metadata={"help": "Path to the folder containing the datasets."},
+    )
+    cutoff_len: int = field(
+        default=2048,
+        metadata={"help": "Cutoff length for the dataset."},
+    )

src/llamafactory/v1/config/model_args.py

@@ -0,0 +1,27 @@
+# Copyright 2025 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from dataclasses import dataclass, field
+
+
+@dataclass
+class ModelArguments:
+    model: str = field(
+        metadata={"help": "Path to the model or model identifier from Hugging Face."},
+    )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={"help": "Trust remote code from Hugging Face."},
+    )

src/llamafactory/v1/config/parser.py

@@ -0,0 +1,63 @@
+# Copyright 2025 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import json
+import sys
+from pathlib import Path
+from typing import Any, Optional, Union
+
+from omegaconf import OmegaConf
+from transformers import HfArgumentParser
+
+from ...extras.misc import is_env_enabled
+from .data_args import DataArguments
+from .model_args import ModelArguments
+from .sample_args import SampleArguments
+from .training_args import TrainingArguments
+
+
+def get_args(
+    args: Optional[Union[dict[str, Any], list[str]]] = None,
+) -> tuple[DataArguments, ModelArguments, TrainingArguments, SampleArguments]:
+    """Parse arguments from command line or config file."""
+    parser = HfArgumentParser([DataArguments, ModelArguments, TrainingArguments, SampleArguments])
+    allow_extra_keys = is_env_enabled("ALLOW_EXTRA_KEYS")
+
+    if args is None:
+        if len(sys.argv) > 1 and (sys.argv[1].endswith(".yaml") or sys.argv[1].endswith(".yml")):
+            override_config = OmegaConf.from_cli(sys.argv[2:])
+            dict_config = OmegaConf.load(Path(sys.argv[1]).absolute())
+            args = OmegaConf.to_container(OmegaConf.merge(dict_config, override_config))
+        elif len(sys.argv) > 1 and sys.argv[1].endswith(".json"):
+            override_config = OmegaConf.from_cli(sys.argv[2:])
+            dict_config = OmegaConf.create(json.load(Path(sys.argv[1]).absolute()))
+            args = OmegaConf.to_container(OmegaConf.merge(dict_config, override_config))
+        else:  # list of strings
+            args = sys.argv[1:]
+
+    if isinstance(args, dict):
+        (*parsed_args,) = parser.parse_dict(args, allow_extra_keys=allow_extra_keys)
+    else:
+        (*parsed_args, unknown_args) = parser.parse_args_into_dataclasses(args, return_remaining_strings=True)
+        if unknown_args and not allow_extra_keys:
+            print(parser.format_help())
+            print(f"Got unknown args, potentially deprecated arguments: {unknown_args}")
+            raise ValueError(f"Some specified arguments are not used by the HfArgumentParser: {unknown_args}")
+
+    return tuple(parsed_args)
+
+
+if __name__ == "__main__":
+    print(get_args())

src/llamafactory/v1/config/sample_args.py

@@ -0,0 +1,24 @@
+# Copyright 2025 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from dataclasses import dataclass, field
+
+
+@dataclass
+class SampleArguments:
+    max_new_tokens: int = field(
+        default=128,
+        metadata={"help": "Maximum number of new tokens to generate."},
+    )

src/llamafactory/v1/config/training_args.py

@@ -0,0 +1,40 @@
+# Copyright 2025 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from dataclasses import dataclass, field
+
+
+@dataclass
+class TrainingArguments:
+    output_dir: str = field(
+        default="",
+        metadata={"help": "Path to the output directory."},
+    )
+    micro_batch_size: int = field(
+        default=1,
+        metadata={"help": "Micro batch size for training."},
+    )
+    global_batch_size: int = field(
+        default=1,
+        metadata={"help": "Global batch size for training."},
+    )
+    learning_rate: float = field(
+        default=1e-4,
+        metadata={"help": "Learning rate for training."},
+    )
+    bf16: bool = field(
+        default=False,
+        metadata={"help": "Use bf16 for training."},
+    )

src/llamafactory/v1/core/base_trainer.py

@@ -0,0 +1,35 @@
+# Copyright 2025 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from ..config.training_args import TrainingArguments
+from ..extras.types import DataLoader, Model, Processor
+
+
+class BaseTrainer:
+    def __init__(
+        self,
+        args: TrainingArguments,
+        model: Model,
+        processor: Processor,
+        data_loader: DataLoader,
+    ) -> None:
+        self.args = args
+        self.model = model
+        self.processor = processor
+        self.data_loader = data_loader
+        self.optimizer = None
+        self.lr_scheduler = None
+
+    def fit(self) -> None:
+        pass

src/llamafactory/v1/core/chat_sampler.py

@@ -0,0 +1,20 @@
+# Copyright 2025 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from ..config.sample_args import SampleArguments
+
+
+class ChatSampler:
+    def __init__(self, sample_args: SampleArguments) -> None:
+        self.args = sample_args

src/llamafactory/v1/core/data_engine.py

@@ -0,0 +1,75 @@
+# Copyright 2025 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+
+from datasets import load_dataset
+from huggingface_hub import hf_hub_download
+from omegaconf import OmegaConf
+
+from ..config.data_args import DataArguments
+from ..extras.types import DataLoader, Dataset, Processor
+
+
+class DataCollator:
+    def __init__(self, processor: Processor) -> None:
+        self.processor = processor
+
+
+class DatasetPathMixin:
+    args: DataArguments
+
+    def _abspath(self, path: str) -> str:
+        return os.path.abspath(os.path.expanduser(os.path.join(self.args.dataset_dir, path)))
+
+    def _exists(self, path: str) -> bool:
+        return os.path.exists(self._abspath(path))
+
+    def _isfile(self, path: str) -> bool:
+        return os.path.isfile(self._abspath(path))
+
+
+class DataEngine(DatasetPathMixin):
+    def __init__(self, data_args: DataArguments) -> None:
+        self.args = data_args
+        self.datasets: dict[str, Dataset] = {}
+        dataset_info = self.get_dataset_info()
+        self.load_dataset(dataset_info)
+
+    def get_dataset_info(self) -> dict:
+        """Get dataset info from dataset path.
+
+        Returns:
+            dict: Dataset info.
+        """
+        if self.args.dataset.endswith(".yaml") and self._isfile(self.args.dataset):  # local file
+            return OmegaConf.load(self._abspath(self.args.dataset))
+        elif self.args.dataset.endswith(".yaml"):  # hf hub uri
+            repo_id, filename = os.path.split(self.args.dataset)
+            filepath = hf_hub_download(repo_id=repo_id, filename=filename, repo_type="dataset")
+            return OmegaConf.load(filepath)
+        elif self._exists(self.args.dataset):  # local file(s)
+            return {"default": {"file_name": self.args.dataset}}
+        else:  # hf hub dataset
+            return {"default": {"hf_hub_url": self.args.dataset}}
+
+    def load_dataset(self, dataset_info: dict) -> None:
+        for key, value in dataset_info.items():
+            if "hf_hub_url" in value:
+                dataset_info[key] = load_dataset(value["hf_hub_url"])
+            elif "file_name" in value:
+                dataset_info[key] = load_dataset(value["file_name"])
+
+    def get_data_loader(self, processor: Processor) -> DataLoader:
+        pass

src/llamafactory/v1/core/model_engine.py

@@ -0,0 +1,27 @@
+# Copyright 2025 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from ..config.model_args import ModelArguments
+from ..extras.types import Model, Processor
+
+
+class ModelEngine:
+    def __init__(self, model_args: ModelArguments) -> None:
+        self.args = model_args
+
+    def get_model(self) -> Model:
+        pass
+
+    def get_processor(self) -> Processor:
+        pass

src/llamafactory/v1/extras/types.py

@@ -0,0 +1,32 @@
+# Copyright 2025 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import TYPE_CHECKING, Union
+
+
+if TYPE_CHECKING:
+    from datasets import Dataset as HFDataset
+    from datasets import IterableDataset
+    from torch.utils.data import DataLoader as TorchDataLoader
+    from transformers import PreTrainedModel, PreTrainedTokenizer, ProcessorMixin
+
+    Dataset = Union[HFDataset, IterableDataset]
+    DataLoader = TorchDataLoader
+    Model = PreTrainedModel
+    Processor = Union[PreTrainedTokenizer, ProcessorMixin]
+else:
+    Dataset = None
+    DataLoader = None
+    Model = None
+    Processor = None

src/llamafactory/v1/launcher.py

@@ -12,22 +12,55 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import sys
 
-def run_train():
+from ..extras.env import VERSION, print_env
-    raise NotImplementedError("Please use `llamafactory-cli sft` or `llamafactory-cli rm`.")
 
 
-def run_chat():
+USAGE = (
-    from llamafactory.v1.core.chat_sampler import Sampler
+    "-" * 70
-
+    + "\n"
-    Sampler().cli()
+    + "| Usage:                                                             |\n"
+    + "|   llamafactory-cli sft -h: train models                            |\n"
+    + "|   llamafactory-cli version: show version info                      |\n"
+    + "| Hint: You can use `lmf` as a shortcut for `llamafactory-cli`.      |\n"
+    + "-" * 70
+)
 
 
-def run_sft():
+WELCOME = (
-    from llamafactory.v1.train.sft import SFTTrainer
+    "-" * 58
+    + "\n"
+    + f"| Welcome to LLaMA Factory, version {VERSION}"
+    + " " * (21 - len(VERSION))
+    + "|\n|"
+    + " " * 56
+    + "|\n"
+    + "| Project page: https://github.com/hiyouga/LLaMA-Factory |\n"
+    + "-" * 58
+)
 
-    SFTTrainer().run()
+
+def launch():
+    command = sys.argv.pop(1) if len(sys.argv) > 1 else "help"
+
+    if command == "sft":
+        from .trainers.sft_trainer import run_sft
+
+        run_sft()
+
+    elif command == "env":
+        print_env()
+
+    elif command == "version":
+        print(WELCOME)
+
+    elif command == "help":
+        print(USAGE)
+
+    else:
+        print(f"Unknown command: {command}.\n{USAGE}")
 
 
 if __name__ == "__main__":
-    run_train()
+    pass

src/llamafactory/v1/plugins/data_plugins/template.py

@@ -0,0 +1,26 @@
+# Copyright 2025 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from dataclasses import dataclass
+
+
+@dataclass
+class Template:
+    user_template: str
+    assistant_template: str
+    system_template: str
+
+    def render_message(self, message: "dict[str, str]") -> str:
+        return self.user_template.format(**message)

src/llamafactory/v1/trainers/sft_trainer.py

@@ -0,0 +1,34 @@
+# Copyright 2025 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from ..config.parser import get_args
+from ..core.base_trainer import BaseTrainer
+from ..core.data_engine import DataEngine
+from ..core.model_engine import ModelEngine
+
+
+class SFTTrainer(BaseTrainer):
+    pass
+
+
+def run_sft():
+    model_args, data_args, training_args, _ = get_args()
+    model_engine = ModelEngine(model_args)
+    data_engine = DataEngine(data_args)
+    model = model_engine.get_model()
+    processor = model_engine.get_processor()
+    data_loader = data_engine.get_data_loader(processor)
+    trainer = SFTTrainer(training_args, model, processor, data_loader)
+    trainer.fit()

src/llamafactory/webui/common.py

@@ -36,8 +36,8 @@ from ..extras.misc import use_modelscope, use_openmind
 
 logger = logging.get_logger(__name__)
 
-DEFAULT_CACHE_DIR = "cache"
+DEFAULT_CACHE_DIR = "llamaboard_cache"
-DEFAULT_CONFIG_DIR = "config"
+DEFAULT_CONFIG_DIR = "llamaboard_config"
 DEFAULT_DATA_DIR = "data"
 DEFAULT_SAVE_DIR = "saves"
 USER_CONFIG = "user_config.yaml"