diff --git a/README.md b/README.md
index 95e5f530c..422185ed6 100644
--- a/README.md
+++ b/README.md
@@ -696,6 +696,12 @@ lora_modules_to_save:
 
 lora_fan_in_fan_out: false
 
+peft:
+  # Configuration options for loftq initialization for LoRA
+  # https://huggingface.co/docs/peft/developer_guides/quantization#loftq-initialization
+  loftq_config:
+    loftq_bits:  # typically 4 bits
+
 # ReLoRA configuration
 # Must use either 'lora' or 'qlora' adapter, and does not support fsdp or deepspeed
 relora_steps: # Number of steps per ReLoRA restart
diff --git a/examples/llama-2/fft_optimized.yml b/examples/llama-2/fft_optimized.yml
index c2639050e..a7e2a6310 100644
--- a/examples/llama-2/fft_optimized.yml
+++ b/examples/llama-2/fft_optimized.yml
@@ -67,6 +67,3 @@ weight_decay: 0.1
 fsdp:
 fsdp_config:
 special_tokens:
-  bos_token: "<s>"
-  eos_token: "</s>"
-  unk_token: "<unk>"
diff --git a/examples/llama-2/loftq.yml b/examples/llama-2/loftq.yml
new file mode 100644
index 000000000..2abbb7847
--- /dev/null
+++ b/examples/llama-2/loftq.yml
@@ -0,0 +1,70 @@
+base_model: NousResearch/Llama-2-7b-hf
+model_type: LlamaForCausalLM
+tokenizer_type: LlamaTokenizer
+is_llama_derived_model: true
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: mhenrichsen/alpaca_2k_test
+    type: alpaca
+dataset_prepared_path:
+val_set_size: 0.05
+output_dir: ./lora-out
+
+sequence_len: 4096
+sample_packing: true
+pad_to_sequence_len: true
+
+adapter: lora
+lora_model_dir:
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_linear: true
+lora_fan_in_fan_out:
+peft:
+  loftq_config:
+    loftq_bits: 4
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 4
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+s2_attention:
+
+warmup_steps: 10
+evals_per_epoch: 4
+eval_table_size:
+eval_table_max_new_tokens: 128
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
diff --git a/examples/llama-2/lora.yml b/examples/llama-2/lora.yml
index 61d82a403..90a9cfd2c 100644
--- a/examples/llama-2/lora.yml
+++ b/examples/llama-2/lora.yml
@@ -65,6 +65,3 @@ weight_decay: 0.0
 fsdp:
 fsdp_config:
 special_tokens:
-  bos_token: "<s>"
-  eos_token: "</s>"
-  unk_token: "<unk>"
diff --git a/examples/llama-2/qlora.yml b/examples/llama-2/qlora.yml
index 41810d56d..badb67ac3 100644
--- a/examples/llama-2/qlora.yml
+++ b/examples/llama-2/qlora.yml
@@ -65,6 +65,3 @@ weight_decay: 0.0
 fsdp:
 fsdp_config:
 special_tokens:
-  bos_token: "<s>"
-  eos_token: "</s>"
-  unk_token: "<unk>"
diff --git a/requirements.txt b/requirements.txt
index b23c2509b..075f63622 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,6 +1,6 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
 packaging==23.2
-peft==0.7.1
+peft @ git+https://github.com/huggingface/peft.git
 transformers==4.37.0
 tokenizers==0.15.0
 bitsandbytes>=0.41.1
diff --git a/src/axolotl/utils/config.py b/src/axolotl/utils/config.py
index 3bc01fc7f..3ea48f6bb 100644
--- a/src/axolotl/utils/config.py
+++ b/src/axolotl/utils/config.py
@@ -232,9 +232,6 @@ def validate_config(cfg):
             "eval_batch_size != micro_batch_size. This can lead to VRAM instability."
         )
 
-    if cfg.load_4bit:
-        raise ValueError("cfg.load_4bit parameter has been deprecated")
-
     if cfg.adapter == "qlora":
         if cfg.merge_lora:
             # can't merge qlora if loaded in 8bit or 4bit
@@ -260,7 +257,8 @@ def validate_config(cfg):
         if cfg.flash_attn_fuse_qkv or cfg.flash_attn_fuse_mlp:
             raise ValueError("Fused modules are not supported with QLoRA")
 
-    if not cfg.load_in_8bit and cfg.adapter == "lora":
+    loftq = cfg.peft and cfg.peft.loftq_config and cfg.peft.loftq_config.loftq_bits
+    if not cfg.load_in_8bit and cfg.adapter == "lora" and not loftq:
         LOG.warning("We recommend setting `load_in_8bit: true` for LORA finetuning")
 
     if cfg.adapter == "lora" and (cfg.flash_attn_fuse_qkv or cfg.flash_attn_fuse_mlp):
diff --git a/src/axolotl/utils/models.py b/src/axolotl/utils/models.py
index 72427f645..e2401b7fe 100644
--- a/src/axolotl/utils/models.py
+++ b/src/axolotl/utils/models.py
@@ -9,7 +9,7 @@ import bitsandbytes as bnb
 import torch
 import transformers
 from optimum.bettertransformer import BetterTransformer
-from peft import PeftConfig, prepare_model_for_kbit_training
+from peft import LoftQConfig, PeftConfig, prepare_model_for_kbit_training
 from peft.tuners.lora import QuantLinear
 from transformers import (  # noqa: F401
     AddedToken,
@@ -667,13 +667,17 @@ def load_model(
         # Qwen doesn't play nicely with LoRA if this is enabled
         skip_prepare_model_for_kbit_training = True
 
-    if (cfg.adapter == "lora" and load_in_8bit) or (
-        cfg.adapter == "qlora" and cfg.load_in_4bit
-    ):
-        LOG.info("converting PEFT model w/ prepare_model_for_kbit_training")
+    loftq_bits = cfg.peft and cfg.peft.loftq_config and cfg.peft.loftq_config.loftq_bits
+    if cfg.adapter == "lora" and loftq_bits:
+        skip_prepare_model_for_kbit_training = True
+
+    if cfg.adapter in ["lora", "qlora"]:
         if cfg.gradient_checkpointing:
             model.gradient_checkpointing_enable()
-        if not skip_prepare_model_for_kbit_training:
+        if (
+            cfg.load_in_8bit or cfg.load_in_4bit
+        ) and not skip_prepare_model_for_kbit_training:
+            LOG.info("converting PEFT model w/ prepare_model_for_kbit_training")
             model = prepare_model_for_kbit_training(
                 model, use_gradient_checkpointing=cfg.gradient_checkpointing
             )
@@ -700,6 +704,7 @@ def load_model(
             model, lora_config = load_adapter(model, cfg, cfg.adapter)
 
     if cfg.ddp and not load_in_8bit and not (cfg.rl and cfg.load_in_4bit):
+        # TODO revaldate this conditional
         model.to(f"cuda:{cfg.local_rank}")
 
     if torch.cuda.device_count() > 1 and int(os.getenv("WORLD_SIZE", "1")) == 1:
@@ -797,6 +802,12 @@ def load_lora(model, cfg, inference=False, config_only=False):
         LOG.info(f"found linear modules: {repr(linear_names)}")
         lora_target_modules = list(set(lora_target_modules + linear_names))
 
+    lora_config_kwargs = {}
+    loftq_bits = cfg.peft and cfg.peft.loftq_config and cfg.peft.loftq_config.loftq_bits
+    if loftq_bits:
+        lora_config_kwargs["loftq_config"] = LoftQConfig(loftq_bits=loftq_bits)
+        lora_config_kwargs["init_lora_weights"] = "loftq"
+
     lora_config = LoraConfig(
         r=cfg.lora_r,
         lora_alpha=cfg.lora_alpha,
@@ -807,6 +818,7 @@ def load_lora(model, cfg, inference=False, config_only=False):
         modules_to_save=cfg.lora_modules_to_save if cfg.lora_modules_to_save else None,
         bias="none",
         task_type="CAUSAL_LM",
+        **lora_config_kwargs,
     )
 
     if config_only:
diff --git a/tests/test_validation.py b/tests/test_validation.py
index d73ae34eb..e5a74394c 100644
--- a/tests/test_validation.py
+++ b/tests/test_validation.py
@@ -32,16 +32,6 @@ class ValidationTest(BaseValidation):
     Test the validation module
     """
 
-    def test_load_4bit_deprecate(self):
-        cfg = DictDefault(
-            {
-                "load_4bit": True,
-            }
-        )
-
-        with pytest.raises(ValueError):
-            validate_config(cfg)
-
     def test_batch_size_unused_warning(self):
         cfg = DictDefault(
             {