811 lines
35 KiB
Python
811 lines
35 KiB
Python
# Copyright 2023-present Daniel Han-Chen & the Unsloth team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from ._utils import (
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is_bfloat16_supported,
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is_vLLM_available,
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HAS_FLASH_ATTENTION,
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HAS_FLASH_ATTENTION_SOFTCAPPING,
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USE_MODELSCOPE,
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get_transformers_model_type,
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)
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from .granite import FastGraniteModel
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from .llama import FastLlamaModel, logger
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from .mistral import FastMistralModel
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from .qwen2 import FastQwen2Model
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from .qwen3 import FastQwen3Model
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from .qwen3_moe import FastQwen3MoeModel
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from .cohere import FastCohereModel
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from transformers import AutoConfig
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from transformers import __version__ as transformers_version
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from peft import PeftConfig, PeftModel
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from .loader_utils import get_model_name
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import os, contextlib, sys
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try:
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from huggingface_hub import get_token
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except:
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try:
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from huggingface_hub.utils import get_token
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except:
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# For older versions of huggingface_hub
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from huggingface_hub.utils._token import get_token
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pass
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pass
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from huggingface_hub import HfFileSystem
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import importlib.util
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# https://github.com/huggingface/transformers/pull/26037 allows 4 bit loading!
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from unsloth_zoo.utils import Version, _get_dtype
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transformers_version = Version(transformers_version)
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SUPPORTS_FOURBIT = transformers_version >= Version("4.37")
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SUPPORTS_GEMMA = transformers_version >= Version("4.38")
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SUPPORTS_GEMMA2 = transformers_version >= Version("4.42")
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SUPPORTS_LLAMA31 = transformers_version >= Version("4.43.2")
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SUPPORTS_LLAMA32 = transformers_version > Version("4.45.0")
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SUPPORTS_GRANITE = transformers_version >= Version("4.46.0")
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SUPPORTS_QWEN3 = transformers_version >= Version("4.50.3")
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SUPPORTS_QWEN3_MOE = transformers_version >= Version("4.50.3")
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if SUPPORTS_GEMMA:
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from .gemma import FastGemmaModel
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if SUPPORTS_GEMMA2:
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from .gemma2 import FastGemma2Model
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pass
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import torch
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from ._utils import (
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patch_compiling_bitsandbytes,
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patch_model_and_tokenizer,
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prepare_model_for_kbit_training,
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patch_unsloth_smart_gradient_checkpointing,
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patch_compiled_autograd,
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process_vision_info,
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unsloth_compile_transformers,
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)
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global FORCE_FLOAT32
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FORCE_FLOAT32 = [
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"gemma3",
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]
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class FastLanguageModel(FastLlamaModel):
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@staticmethod
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def from_pretrained(
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model_name = "unsloth/Llama-3.2-1B-Instruct",
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max_seq_length = 2048,
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dtype = None,
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load_in_4bit = True,
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load_in_8bit = False,
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full_finetuning = False,
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token = None,
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device_map = "sequential",
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rope_scaling = None,
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fix_tokenizer = True,
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trust_remote_code = False,
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use_gradient_checkpointing = "unsloth",
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resize_model_vocab = None,
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revision = None,
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use_exact_model_name = False,
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fast_inference = False, # uses vLLM
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gpu_memory_utilization = 0.5,
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float8_kv_cache = False,
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random_state = 3407,
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max_lora_rank = 64,
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disable_log_stats = True,
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*args, **kwargs,
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):
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if load_in_8bit or full_finetuning:
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return FastModel.from_pretrained(
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model_name = model_name,
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max_seq_length = max_seq_length,
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dtype = dtype,
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load_in_4bit = load_in_4bit,
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load_in_8bit = load_in_8bit,
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full_finetuning = full_finetuning,
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token = token,
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device_map = device_map,
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rope_scaling = rope_scaling, # [TODO] No effect
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fix_tokenizer = fix_tokenizer, # [TODO] No effect
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trust_remote_code = trust_remote_code,
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use_gradient_checkpointing = use_gradient_checkpointing,
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resize_model_vocab = resize_model_vocab, # [TODO] No effect
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revision = revision,
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return_logits = False, # Return logits
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fullgraph = True, # No graph breaks
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use_exact_model_name = use_exact_model_name,
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*args, **kwargs,
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)
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pass
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if token is None: token = get_token()
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assert (dtype is None or dtype == torch.float16 or dtype == torch.bfloat16)
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if use_gradient_checkpointing == "unsloth":
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patch_unsloth_smart_gradient_checkpointing(dtype = dtype)
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if fast_inference:
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if importlib.util.find_spec("vllm") is None:
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raise ImportError(
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"Unsloth: Please install vLLM before enabling `fast_inference`!\n"\
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"You can do this in a terminal via `pip install vllm`"
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)
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pass
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pass
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old_model_name = model_name
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if not use_exact_model_name:
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model_name = get_model_name(model_name, load_in_4bit)
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if USE_MODELSCOPE and not os.path.exists(model_name):
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from modelscope import snapshot_download
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model_name = snapshot_download(model_name)
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pass
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# First check if it's a normal model via AutoConfig
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from huggingface_hub.utils import disable_progress_bars, enable_progress_bars, are_progress_bars_disabled
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was_disabled = are_progress_bars_disabled()
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disable_progress_bars()
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autoconfig_error = None
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peft_error = None
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try:
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model_config = AutoConfig.from_pretrained(
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model_name,
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token = token,
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revision = revision,
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trust_remote_code = trust_remote_code,
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)
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is_model = True
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except Exception as error:
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autoconfig_error = str(error)
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is_model = False
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try:
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peft_config = PeftConfig.from_pretrained(
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model_name,
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token = token,
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revision = revision,
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trust_remote_code = trust_remote_code,
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)
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is_peft = True
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except Exception as error:
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peft_error = str(error)
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is_peft = False
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pass
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# Both config.json and adapter_config.json should not exist!
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# Old transformers versions check
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both_exist = (is_model and is_peft) and not SUPPORTS_LLAMA32
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# New transformers need to check manually.
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if SUPPORTS_LLAMA32:
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# Check if folder exists locally
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if os.path.isdir(model_name):
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exist_adapter_config = os.path.exists(os.path.join(model_name, "adapter_config.json"))
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exist_config = os.path.exists(os.path.join(model_name, "config.json"))
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both_exist = exist_adapter_config and exist_config
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else:
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# Because HfFileSystem assumes linux paths, we need to set the path with forward slashes, even on Windows.
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files = HfFileSystem(token = token).glob(f"{model_name}/*.json")
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files = (os.path.split(x)[-1] for x in files)
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if sum(x == "adapter_config.json" or x == "config.json" for x in files) >= 2:
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both_exist = True
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pass
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pass
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pass
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# Error out if both LoRA and normal model config exists.
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if both_exist:
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raise RuntimeError(
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"Unsloth: Your repo has a LoRA adapter and a base model.\n"\
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"You have 2 files `config.json` and `adapter_config.json`.\n"\
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"We must only allow one config file.\n"\
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"Please separate the LoRA and base models to 2 repos."
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)
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elif not is_model and not is_peft:
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error = autoconfig_error or peft_error
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# Old transformers version
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if "rope_scaling" in error.lower() and not SUPPORTS_LLAMA31:
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raise ImportError(
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f"Unsloth: Your transformers version of {transformers_version} does not support new RoPE scaling methods.\n"\
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f"This includes Llama 3.1. The minimum required version is 4.43.2\n"\
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f'Try `pip install --upgrade "transformers>=4.43.2"`\n'\
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f"to obtain the latest transformers build, then restart this session."\
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)
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# Create a combined error message showing both failures
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combined_error = (
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"Unsloth: Failed to load model. Both AutoConfig and PeftConfig loading failed.\n\n"
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f"AutoConfig error: {autoconfig_error}\n\n"
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f"PeftConfig error: {peft_error}\n\n"
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)
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raise RuntimeError(combined_error)
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pass
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# Get base model for PEFT:
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if is_peft:
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# Check base model again for PEFT
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model_name = peft_config.base_model_name_or_path
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if not use_exact_model_name:
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model_name = get_model_name(model_name, load_in_4bit)
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model_config = AutoConfig.from_pretrained(
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model_name,
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token = token,
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trust_remote_code = trust_remote_code,
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)
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pass
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if not was_disabled: enable_progress_bars()
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model_type = model_config.model_type
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if model_type == "llama":
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scaling_type = None
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if getattr(model_config, "rope_scaling", None) is not None:
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scaling_type1 = model_config.rope_scaling.get("type", None)
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scaling_type2 = model_config.rope_scaling.get("rope_type", None)
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scaling_type = scaling_type1 if scaling_type1 is not None else scaling_type2
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pass
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if scaling_type == "llama3" and not SUPPORTS_LLAMA31:
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raise ImportError(
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f"Unsloth: Your transformers version of {transformers_version} does not support Llama 3.1.\n"\
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f"The minimum required version is 4.43.2\n"\
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f'Try `pip install --upgrade "transformers>=4.43.2"`\n'\
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f"to obtain the latest transformers build, then restart this session."\
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)
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dispatch_model = FastLlamaModel
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elif model_type == "mistral": dispatch_model = FastMistralModel
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elif model_type == "gemma":
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if not SUPPORTS_GEMMA:
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raise ImportError(
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f"Unsloth: Your transformers version of {transformers_version} does not support Gemma.\n"\
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f"The minimum required version is 4.38.\n"\
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f'Try `pip install --upgrade "transformers>=4.38"`\n'\
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f"to obtain the latest transformers build, then restart this session."\
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)
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dispatch_model = FastGemmaModel
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elif model_type == "gemma2":
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if not SUPPORTS_GEMMA2:
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raise ImportError(
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f"Unsloth: Your transformers version of {transformers_version} does not support Gemma2.\n"\
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f"The minimum required version is 4.42.3.\n"\
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f'Try `pip install --upgrade "transformers>=4.42.3"`\n'\
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f"to obtain the latest transformers build, then restart this session."\
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)
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# Also check for softcapping support in flash-attn which is faster!
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if is_bfloat16_supported() and not HAS_FLASH_ATTENTION:
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print(
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"Unsloth: If you want to finetune Gemma 2, install flash-attn to make it faster!\n"\
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"To install flash-attn, do the below:\n"\
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'\npip install --no-deps --upgrade "flash-attn>=2.6.3"'
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)
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elif HAS_FLASH_ATTENTION and not HAS_FLASH_ATTENTION_SOFTCAPPING:
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print(
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"Unsloth: If you want to finetune Gemma 2, upgrade flash-attn to version 2.6.3 or higher!\n"\
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"Newer versions support faster and less memory usage kernels for Gemma 2's attention softcapping!\n"\
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"To update flash-attn, do the below:\n"\
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'\npip install --no-deps --upgrade "flash-attn>=2.6.3"'
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)
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dispatch_model = FastGemma2Model
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elif model_type == "qwen2":
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dispatch_model = FastQwen2Model
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elif model_type == "qwen3":# or model_type == "qwen3_moe":
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if not SUPPORTS_QWEN3 or not SUPPORTS_QWEN3_MOE:
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raise ImportError(
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f"Unsloth: Your transformers version of {transformers_version} does not support Qwen3.\n"\
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f"The minimum required version is 4.50.3.\n"\
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f'Try `pip install --upgrade "transformers>=4.50.3"`\n'\
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f"to obtain the latest transformers build, then restart this session."\
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)
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dispatch_model = FastQwen3Model if model_type == "qwen3" else FastQwen3MoeModel
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# Temporary disable optimized Cohere until errors match
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# elif model_type == "cohere":
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# dispatch_model = FastCohereModel
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# Temporary disable optimized Granite until errors match
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# elif model_type == "granite":
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# dispatch_model = FastGraniteModel
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else:
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return FastModel.from_pretrained(
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model_name = model_name,
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max_seq_length = max_seq_length,
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dtype = dtype,
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load_in_4bit = load_in_4bit,
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load_in_8bit = load_in_8bit,
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full_finetuning = full_finetuning,
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token = token,
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device_map = device_map,
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rope_scaling = rope_scaling, # [TODO] No effect
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fix_tokenizer = fix_tokenizer, # [TODO] No effect
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trust_remote_code = trust_remote_code,
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use_gradient_checkpointing = use_gradient_checkpointing,
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resize_model_vocab = resize_model_vocab, # [TODO] No effect
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revision = revision,
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return_logits = False, # Return logits
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fullgraph = True, # No graph breaks
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use_exact_model_name = use_exact_model_name,
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*args, **kwargs,
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)
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pass
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# Check if this is local model since the tokenizer gets overwritten
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if os.path.exists(os.path.join(old_model_name, "tokenizer_config.json")) and \
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os.path.exists(os.path.join(old_model_name, "tokenizer.json")) and \
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os.path.exists(os.path.join(old_model_name, "special_tokens_map.json")):
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tokenizer_name = old_model_name
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else:
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tokenizer_name = None
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pass
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if fast_inference:
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if not is_vLLM_available():
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print("Unsloth: vLLM is not installed! Will use Unsloth inference!")
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fast_inference = False
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pass
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from unsloth_zoo.vllm_utils import (
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patch_vllm,
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vllm_dynamic_quant_supported,
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)
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patch_vllm()
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if model_name.endswith("unsloth-bnb-4bit"):
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if not vllm_dynamic_quant_supported(model_name, model_config):
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# Instead use -bnb-4bit variant
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print(
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f"Unsloth: Switching from Unsloth dynamic quant to normal quant since\n"\
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f"we do not yet support fast inference for {model_name}"
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)
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model_name = model_name[:-len("unsloth-bnb-4bit")] + "bnb-4bit"
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pass
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pass
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pass
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model, tokenizer = dispatch_model.from_pretrained(
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model_name = model_name,
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max_seq_length = max_seq_length,
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dtype = _get_dtype(dtype),
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load_in_4bit = load_in_4bit,
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token = token,
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device_map = device_map,
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rope_scaling = rope_scaling,
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fix_tokenizer = fix_tokenizer,
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model_patcher = dispatch_model,
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tokenizer_name = tokenizer_name,
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trust_remote_code = trust_remote_code,
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revision = revision if not is_peft else None,
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fast_inference = fast_inference,
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gpu_memory_utilization = gpu_memory_utilization,
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float8_kv_cache = float8_kv_cache,
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random_state = random_state,
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max_lora_rank = max_lora_rank,
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disable_log_stats = disable_log_stats,
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*args, **kwargs,
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)
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if resize_model_vocab is not None:
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model.resize_token_embeddings(resize_model_vocab)
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pass
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# In case the model supports tagging, add the unsloth tag.
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if hasattr(model, "add_model_tags"):
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model.add_model_tags(["unsloth",])
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pass
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if hasattr(tokenizer, "add_model_tags"):
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tokenizer.add_model_tags(["unsloth",])
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pass
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if load_in_4bit:
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# Fix up bitsandbytes config
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quantization_config = \
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{
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# Sometimes torch_dtype is not a string!!
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"bnb_4bit_compute_dtype" : model.config.to_dict()["torch_dtype"],
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"bnb_4bit_quant_type" : "nf4",
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"bnb_4bit_use_double_quant" : True,
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"llm_int8_enable_fp32_cpu_offload" : False,
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"llm_int8_has_fp16_weight" : False,
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"llm_int8_skip_modules" : None,
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"llm_int8_threshold" : 6.0,
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"load_in_4bit" : True,
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"load_in_8bit" : False,
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"quant_method" : "bitsandbytes",
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}
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model.config.update({"quantization_config" : quantization_config})
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pass
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if is_peft:
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# From https://github.com/huggingface/peft/issues/184
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# Now add PEFT adapters
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model = PeftModel.from_pretrained(
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model,
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old_model_name,
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token = token,
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revision = revision,
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is_trainable = True,
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trust_remote_code = trust_remote_code,
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)
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# Patch it as well!
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model = dispatch_model.patch_peft_model(model, use_gradient_checkpointing)
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pass
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return model, tokenizer
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pass
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pass
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from ..kernels import (
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patch_loss_functions,
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|
post_patch_loss_function,
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)
|
|
from .vision import FastBaseModel
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|
from transformers import (
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|
AutoModelForCausalLM,
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|
)
|
|
try:
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|
from transformers import AutoModelForImageTextToText
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|
AutoModelForVision2Seq = AutoModelForImageTextToText
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|
except:
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from transformers import AutoModelForVision2Seq
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|
pass
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|
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class FastModel(FastBaseModel):
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@staticmethod
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def from_pretrained(
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model_name = "unsloth/Llama-3.2-11B-Vision-Instruct-bnb-4bit",
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max_seq_length = 2048,
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dtype = None,
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load_in_4bit = True,
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load_in_8bit = False,
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full_finetuning = False,
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token = None,
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device_map = "sequential",
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rope_scaling = None, # [TODO] No effect
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fix_tokenizer = True, # [TODO] No effect
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trust_remote_code = False,
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use_gradient_checkpointing = "unsloth",
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resize_model_vocab = None, # [TODO] No effect
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revision = None,
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return_logits = False, # Return logits
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fullgraph = True, # No graph breaks
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use_exact_model_name = False,
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auto_model = None,
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whisper_language = None,
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whisper_task = None,
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is_multimodal = None,
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*args, **kwargs,
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):
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if token is None: token = get_token()
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if whisper_language is not None: assert(type(whisper_language) is str)
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if whisper_task is not None: assert(type(whisper_task) is str)
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SUPPORTS_BFLOAT16 = is_bfloat16_supported()
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if dtype is None:
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dtype = torch.float16 if not SUPPORTS_BFLOAT16 else torch.bfloat16
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elif dtype == torch.bfloat16 and not SUPPORTS_BFLOAT16:
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logger.warning_once("Device does not support bfloat16. Will change to float16.")
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dtype = torch.float16
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assert(dtype in (torch.float16, torch.bfloat16, torch.float32))
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patch_compiled_autograd()
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patch_compiling_bitsandbytes()
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if full_finetuning and (load_in_4bit or load_in_8bit):
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print("Unsloth: You selected full finetuning support, but 4bit / 8bit is enabled - disabling LoRA / QLoRA.")
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load_in_4bit = False
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load_in_8bit = False
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pass
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if load_in_4bit and load_in_8bit:
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raise RuntimeError(
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"Unsloth: Can only load in 4bit or 8bit, not both!\n"\
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"Also, we by default set `load_in_4bit = True`.\n"\
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"If you want 8bit finetuning, set both `load_in_4bit = False` and `load_in_8bit = True`"
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)
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pass
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old_model_name = model_name
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if not use_exact_model_name:
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model_name = get_model_name(model_name, load_in_4bit)
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# Check versions
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LATEST = '\nPlease use transformers via `pip install --no-deps git+https://github.com/huggingface/transformers.git`'
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NIGHTLY = '\nPlease use nightly transformers via pip install --upgrade "transformers>=4.49.0"`'
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if "pixtral" in model_name.lower() and transformers_version < Version("4.49.0"):
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raise RuntimeError("Unsloth: Pixtral only works on transformers >= 4.49.0." + LATEST)
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elif "qwen2.5" in model_name.lower() and transformers_version < Version("4.49.0"):
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raise RuntimeError("Unsloth: Qwen 2.5 only works on transformers >= 4.49.0." + LATEST)
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elif "aya-vision" in model_name.lower():
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# Disable compiling for now - errors out!
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os.environ["UNSLOTH_COMPILE_DISABLE"] = "1"
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if transformers_version < Version("4.50.0.dev0"):
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raise RuntimeError("Unsloth: Aya Vision only works on transformers >= 4.50.0." + NIGHTLY)
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elif "gemma-3" in model_name.lower() and transformers_version < Version("4.50.0.dev0"):
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raise RuntimeError("Unsloth: Gemma 3 only works on transformers >= 4.50.0." + NIGHTLY)
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elif "c4ai-command-a-03-2025" in model_name.lower() and transformers_version < Version("4.50.0.dev0"):
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raise RuntimeError("Unsloth: Cohere's Command model only works on transformers >= 4.50.0." + NIGHTLY)
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elif "granite-vision" in model_name.lower():
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# Disable compiling for now - errors out!
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os.environ["UNSLOTH_COMPILE_DISABLE"] = "1"
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if transformers_version < Version("4.50.0.dev0"):
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raise RuntimeError("Unsloth: Granite Vision only works on transformers >= 4.50.0." + NIGHTLY)
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elif "csm-1b" in model_name.lower():
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os.environ["UNSLOTH_COMPILE_DISABLE"] = "1"
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os.environ["UNSLOTH_DISABLE_FAST_GENERATION"] = "1"
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os.environ["UNSLOTH_FORCE_CUSTOM_DTYPE"] = "torch.float16;if name.endswith(('_proj', 'fc1', 'fc2', 'codebook', 'head')): module.to(torch.float16)"
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elif "olmo-2" in model_name.lower() and transformers_version < Version("4.50.0.dev0"):
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raise RuntimeError("Unsloth: OLMo-2 only works on transformers >= 4.50.0." + NIGHTLY)
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pass
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if USE_MODELSCOPE and not os.path.exists(model_name):
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from modelscope import snapshot_download
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model_name = snapshot_download(model_name)
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pass
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# First check if it's a normal model via AutoConfig
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from huggingface_hub.utils import disable_progress_bars, enable_progress_bars, are_progress_bars_disabled
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was_disabled = are_progress_bars_disabled()
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disable_progress_bars()
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autoconfig_error = None
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peft_error = None
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try:
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model_config = AutoConfig.from_pretrained(
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model_name,
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token = token,
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revision = revision,
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trust_remote_code = trust_remote_code,
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)
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is_model = True
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except Exception as error:
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autoconfig_error = str(error)
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is_model = False
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try:
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peft_config = PeftConfig.from_pretrained(
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model_name,
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token = token,
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revision = revision,
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trust_remote_code = trust_remote_code,
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)
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is_peft = True
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except Exception as error:
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peft_error = str(error)
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is_peft = False
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pass
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# Both config.json and adapter_config.json should not exist!
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# Old transformers versions check
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both_exist = (is_model and is_peft) and not SUPPORTS_LLAMA32
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# New transformers need to check manually.
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if SUPPORTS_LLAMA32:
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# Check if folder exists locally
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if os.path.isdir(model_name):
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exist_adapter_config = os.path.exists(os.path.join(model_name, "adapter_config.json"))
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exist_config = os.path.exists(os.path.join(model_name, "config.json"))
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both_exist = exist_adapter_config and exist_config
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else:
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files = HfFileSystem(token = token).glob(f"{model_name}/*.json")
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files = (os.path.split(x)[-1] for x in files)
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if sum(x == "adapter_config.json" or x == "config.json" for x in files) >= 2:
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both_exist = True
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pass
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pass
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pass
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# Error out if both LoRA and normal model config exists.
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if both_exist:
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raise RuntimeError(
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"Unsloth: Your repo has a LoRA adapter and a base model.\n"\
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"You have 2 files `config.json` and `adapter_config.json`.\n"\
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"We must only allow one config file.\n"\
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"Please separate the LoRA and base models to 2 repos."
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)
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elif not is_model and not is_peft:
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error = autoconfig_error or peft_error
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# Old transformers version
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if "rope_scaling" in error.lower() and not SUPPORTS_LLAMA31:
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raise ImportError(
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f"Unsloth: Your transformers version of {transformers_version} does not support new RoPE scaling methods.\n"\
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f"This includes Llama 3.1. The minimum required version is 4.43.2\n"\
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f'Try `pip install --upgrade "transformers>=4.43.2"`\n'\
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f"to obtain the latest transformers build, then restart this session."\
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)
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# Create a combined error message showing both failures
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combined_error = (
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"Unsloth: Failed to load model. Both AutoConfig and PeftConfig loading failed.\n\n"
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f"AutoConfig error: {autoconfig_error}\n\n"
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f"PeftConfig error: {peft_error}\n\n"
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)
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raise RuntimeError(combined_error)
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pass
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# Get base model for PEFT:
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if is_peft:
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# Check base model again for PEFT
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model_name = peft_config.base_model_name_or_path
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if not use_exact_model_name:
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model_name = get_model_name(model_name, load_in_4bit)
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model_config = AutoConfig.from_pretrained(
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model_name,
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token = token,
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trust_remote_code = trust_remote_code,
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)
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pass
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if not was_disabled: enable_progress_bars()
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do_logging = os.environ.get("UNSLOTH_ENABLE_LOGGING", "0") == "1"
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if do_logging:
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redirector = contextlib.nullcontext()
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else:
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redirector = contextlib.redirect_stdout(open(os.devnull, "w"))
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# Get model types like Gemma3 etc
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model_types = get_transformers_model_type(
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model_name = model_name,
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token = token,
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revision = revision,
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trust_remote_code = trust_remote_code,
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)
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model_types = ["siglip"] + model_types
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# Set forced float32 env flag
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os.environ["UNSLOTH_FORCE_FLOAT32"] = "0"
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do_forced_float32 = False
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for model_type_arch in model_types:
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if model_type_arch != "siglip": break
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global FORCE_FLOAT32
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for disable_name in FORCE_FLOAT32:
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if (disable_name.lower() == model_type_arch.lower() or \
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disable_name.lower() in model_name.lower()) and \
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((dtype == torch.float16) or not SUPPORTS_BFLOAT16):
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os.environ["UNSLOTH_FORCE_FLOAT32"] = "1"
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dtype = torch.bfloat16 # Change to bfloat16 loading
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break
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pass
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# Patch gradient checkpointing
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if use_gradient_checkpointing == "unsloth":
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patch_unsloth_smart_gradient_checkpointing(dtype = dtype)
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with redirector:
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patch_loss_functions(torch_compile = False)
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model_types, supports_sdpa = unsloth_compile_transformers(
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dtype = dtype,
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model_name = model_name,
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model_types = model_types,
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token = token,
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sdpa_dynamic_mask = True,
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sdpa_bool_masks = True,
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sdpa_gqa_replace = True,
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sdpa_dynamic_compile = True,
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compile_attention = True,
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disable_causal_masks = True,
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compile_torch_modules = True,
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compile_custom_modules = True,
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compile_function_calls = True,
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fuse_lm_head = True,
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gradient_checkpointing = True,
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manual_replacements = True,
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fast_lora_forwards = True,
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fast_residual_stream = False,
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accurate_accumulation = True,
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epilogue_fusion = True,
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max_autotune = False,
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shape_padding = True,
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cudagraphs = False,
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debug = False,
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fullgraph = fullgraph,
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import_from_cache = False,
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disable = False,
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return_logits = return_logits,
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trust_remote_code = trust_remote_code,
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)
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pass
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# Check if this is local model since the tokenizer gets overwritten
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if os.path.exists(os.path.join(old_model_name, "tokenizer_config.json")) and \
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os.path.exists(os.path.join(old_model_name, "tokenizer.json")) and \
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os.path.exists(os.path.join(old_model_name, "special_tokens_map.json")):
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tokenizer_name = old_model_name
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else:
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tokenizer_name = None
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pass
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# Check if VLM
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is_vlm = any(x.endswith("ForConditionalGeneration") for x in model_config.architectures)
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is_vlm = is_vlm or hasattr(model_config, "vision_config")
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if auto_model is None:
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auto_model = AutoModelForVision2Seq if is_vlm else AutoModelForCausalLM
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model, tokenizer = FastBaseModel.from_pretrained(
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model_name = model_name,
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max_seq_length = max_seq_length,
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dtype = _get_dtype(dtype),
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load_in_4bit = load_in_4bit,
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load_in_8bit = load_in_8bit,
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full_finetuning = full_finetuning,
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token = token,
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device_map = device_map,
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trust_remote_code = trust_remote_code,
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revision = revision if not is_peft else None,
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model_types = model_types,
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tokenizer_name = tokenizer_name,
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auto_model = auto_model,
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use_gradient_checkpointing = use_gradient_checkpointing,
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supports_sdpa = supports_sdpa,
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whisper_language = whisper_language,
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whisper_task = whisper_task,
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*args, **kwargs,
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)
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if resize_model_vocab is not None:
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model.resize_token_embeddings(resize_model_vocab)
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pass
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# In case the model supports tagging, add the unsloth tag.
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if hasattr(model, "add_model_tags"):
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model.add_model_tags(["unsloth",])
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pass
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if hasattr(tokenizer, "add_model_tags"):
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tokenizer.add_model_tags(["unsloth",])
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pass
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if load_in_4bit:
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# Fix up bitsandbytes config
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quantization_config = \
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{
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# Sometimes torch_dtype is not a string!!
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"bnb_4bit_compute_dtype" : model.config.to_dict()["torch_dtype"],
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"bnb_4bit_quant_type" : "nf4",
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"bnb_4bit_use_double_quant" : True,
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"llm_int8_enable_fp32_cpu_offload" : False,
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"llm_int8_has_fp16_weight" : False,
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"llm_int8_skip_modules" : None,
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"llm_int8_threshold" : 6.0,
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"load_in_4bit" : True,
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"load_in_8bit" : False,
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"quant_method" : "bitsandbytes",
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}
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model.config.update({"quantization_config" : quantization_config})
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pass
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if is_peft:
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# From https://github.com/huggingface/peft/issues/184
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# Now add PEFT adapters
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model = PeftModel.from_pretrained(
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model,
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old_model_name,
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token = token,
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revision = revision,
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is_trainable = True,
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trust_remote_code = trust_remote_code,
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)
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# Patch it as well!
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model = FastBaseModel.post_patch_model(model, use_gradient_checkpointing)
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pass
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return model, tokenizer
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pass
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pass
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class FastVisionModel(FastModel):
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pass
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class FastTextModel(FastModel):
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pass
|