diff --git a/scripts/finetune.py b/scripts/finetune.py
index 0a5f318639..0ad10dab8c 100644
--- a/scripts/finetune.py
+++ b/scripts/finetune.py
@@ -57,27 +57,38 @@ def get_multi_line_input() -> Optional[str]:
     # instruction = pathlib.Path("/proc/self/fd/0").read_text()
     return instruction
 
+def get_merged_out_dir(cfg: DictDefault):
+    return Path(cfg.output_dir) / "merged"
 
-def do_merge_lora(
+def do_merge_lora_model_and_tokenizer(
     *,
     cfg: DictDefault,
-    cli_args: TrainerCliArgs,
+    model,
+    tokenizer,
 ):
-    model, tokenizer = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
     safe_serialization = cfg.save_safetensors is True
 
     LOG.info("running merge of LoRA with base model")
     model = model.merge_and_unload()
     model.to(dtype=torch.float16)
 
+    merged_out_dir = str(get_merged_out_dir(cfg))
+
     if cfg.local_rank == 0:
         LOG.info("saving merged model")
         model.save_pretrained(
-            str(Path(cfg.output_dir) / "merged"),
+            merged_out_dir,
             safe_serialization=safe_serialization,
         )
-        tokenizer.save_pretrained(str(Path(cfg.output_dir) / "merged"))
+        tokenizer.save_pretrained(merged_out_dir)
 
+def do_merge_lora(
+    *,
+    cfg: DictDefault,
+    cli_args: TrainerCliArgs,
+):
+    model, tokenizer = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
+    do_merge_lora_model_and_tokenizer(cfg=cfg, model=model, tokenizer=tokenizer)
 
 def shard(
     *,
diff --git a/scripts/quantize.py b/scripts/quantize.py
new file mode 100644
index 0000000000..56106cc655
--- /dev/null
+++ b/scripts/quantize.py
@@ -0,0 +1,281 @@
+# pip install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
+
+# import debugpy
+# debugpy.listen(('0.0.0.0', 5678))
+# debugpy.wait_for_client()
+# debugpy.breakpoint()
+
+import json
+import random
+import time
+from pathlib import Path
+import logging
+
+import torch
+from datasets import load_dataset, Dataset
+from transformers import AutoTokenizer, LlamaTokenizer, TextGenerationPipeline
+from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
+from axolotl.prompters import AlpacaPrompter
+from axolotl.utils.models import load_model, load_tokenizer
+from axolotl.common.cli import TrainerCliArgs
+from axolotl.logging_config import configure_logging
+from axolotl.utils.dict import DictDefault
+# from scripts.finetune import load_cfg
+from finetune import load_cfg, get_merged_out_dir, do_merge_lora_model_and_tokenizer, load_datasets
+
+configure_logging()
+LOG = logging.getLogger("axolotl")
+
+# logging.basicConfig(
+#     format="%(asctime)s %(levelname)s [%(name)s] %(message)s", level=logging.DEBUG, datefmt="%Y-%m-%d %H:%M:%S"
+# )
+
+# LOG.setLevel(logging.DEBUG)
+# handler = logging.StreamHandler()
+# formatter = logging.Formatter('%(asctime)s %(levelname)s [%(name)s] %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
+# handler.setFormatter(formatter)
+# LOG.addHandler(handler)
+
+print("Done importing...")
+
+## CHANGE BELOW ##
+config_path: Path = Path("./examples/llama-2/lora.yml")
+
+# pretrained_model_dir = "facebook/opt-125m"
+# quantized_model_dir = "opt-125m-4bit"
+dataset_name = "teknium/GPT4-LLM-Cleaned"
+# huggingface_username = "CHANGE_ME"
+## CHANGE ABOVE
+
+quantize_config = BaseQuantizeConfig(
+    bits=4,  # quantize model to 4-bit
+    group_size=128,  # it is recommended to set the value to 128
+    desc_act=False,  # set to False can significantly speed up inference but the perplexity may slightly bad 
+)
+
+# TEMPLATE = "<|prompt|>{instruction}</s><|answer|>"
+prompter = AlpacaPrompter()
+
+# def load_data(data_path, tokenizer, n_samples, template=TEMPLATE):
+def load_data(data_path, tokenizer, n_samples):
+    # Load dataset
+    dataset = load_dataset(data_path)
+    
+    if "train" in dataset:
+        raw_data = dataset["train"]
+    else:
+        raw_data = dataset
+
+    # Sample from the dataset if n_samples is provided and less than the dataset size
+    if n_samples is not None and n_samples < len(raw_data):
+        raw_data = raw_data.shuffle(seed=42).select(range(n_samples))
+
+    def tokenize(examples):
+        instructions = examples["instruction"]
+        outputs = examples["output"]
+
+        prompts = []
+        texts = []
+        input_ids = []
+        attention_mask = []
+        for input_text, output_text in zip(instructions, outputs):
+            # prompt = template.format(instruction=input_text)
+            # prompt = next(prompter.build_prompt(instruction=input_text, output=output_text))
+            prompt = next(prompter.build_prompt(instruction=input_text))
+            text = prompt + output_text
+
+            if len(tokenizer(prompt)["input_ids"]) >= tokenizer.model_max_length:
+                continue
+
+            tokenized_data = tokenizer(text)
+
+            input_ids.append(tokenized_data["input_ids"][: tokenizer.model_max_length])
+            attention_mask.append(tokenized_data["attention_mask"][: tokenizer.model_max_length])
+            prompts.append(prompt)
+            texts.append(text)
+
+        return {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "prompt": prompts,
+            "text": texts,
+        }
+
+    raw_data = raw_data.map(
+        tokenize,
+        batched=True,
+        batch_size=len(raw_data),
+        num_proc=1,
+        keep_in_memory=True,
+        load_from_cache_file=False,
+        # remove_columns=["instruction", "input"]
+    )
+
+    # Convert to PyTorch tensors
+    raw_data.set_format(type='torch', columns=['input_ids', 'attention_mask'])
+
+    # for sample in dataset:
+    #     sample["input_ids"] = torch.LongTensor(sample["input_ids"])
+    #     sample["attention_mask"] = torch.LongTensor(sample["attention_mask"])
+
+    return raw_data
+
+
+# def get_tokenizer():
+#     print("Loading tokenizer...")
+#     # tokenizer = AutoTokenizer.from_pretrained(pretrained_model_dir, use_fast=True)
+#     tokenizer = LlamaTokenizer.from_pretrained(pretrained_model_dir, use_fast=True)
+#     return tokenizer
+
+# def get_model():
+def load_merged_model(cfg: DictDefault):
+    print("Loading model...")
+
+    merged_out_dir = get_merged_out_dir(cfg)
+    
+    # Check if the merged model exists
+    if not merged_out_dir.exists():
+        # If not, merge the model
+        print("Merged model not found. Merging...")
+        # model, tokenizer = load_model(cfg, inference=True)
+        # do_merge_lora_model_and_tokenizer(cfg=cfg, model=model, tokenizer=tokenizer)
+        raise NotImplementedError("Merging model is not implemented yet.")
+
+    # load un-quantized model, by default, the model will always be loaded into CPU memory
+    model = AutoGPTQForCausalLM.from_pretrained(merged_out_dir, quantize_config)
+    # model = AutoGPTQForCausalLM.from_pretrained(pretrained_model_dir, quantize_config)
+    print("Model loaded.")
+    return model
+
+def get_quantized_model(cfg: DictDefault):
+    print("Loading quantized model...")
+    quantized_model_dir = get_quantized_model_dir(cfg)
+    model = AutoGPTQForCausalLM.from_quantized(quantized_model_dir, device="cuda:0", use_safetensors=True)
+    print("Model loaded.")
+    return model
+
+def quantize_and_save(cfg: DictDefault, model, tokenizer, examples_for_quant):
+    print("Quantize...")
+    start = time.time()
+    # quantize model, the examples should be list of dict whose keys can only be "input_ids" and "attention_mask"
+    model.quantize(
+        examples_for_quant,
+        batch_size=1,
+        # batch_size=args.quant_batch_size,
+        # use_triton=args.use_triton,
+        # autotune_warmup_after_quantized=args.use_triton
+    )
+    end = time.time()
+    print(f"quantization took: {end - start: .4f}s")
+
+    # save quantized model
+    print("Saving quantized model...")
+    # model.save_quantized(quantized_model_dir)
+    quantized_model_dir = get_quantized_model_dir(cfg)
+    model.save_quantized(quantized_model_dir, use_safetensors=True)
+    print("Saving tokenizer...")
+    tokenizer.save_pretrained(quantized_model_dir)
+    print("Saved.")
+
+    return model
+
+def push_model(cfg: DictDefault, model, tokenizer):
+# def push_model(model):
+    # push quantized model to Hugging Face Hub. 
+    # to use use_auth_token=True, Login first via huggingface-cli login.
+    # or pass explcit token with: use_auth_token="hf_xxxxxxx"
+    # (uncomment the following three lines to enable this feature)
+    # repo_id = f"YourUserName/{quantized_model_dir}"
+    print("Pushing to Huggingface hub...")
+    # repo_id = f"{huggingface_username}/{quantized_model_dir}"
+    repo_id = get_quantized_model_id(cfg)
+    pretrained_model_dir = cfg['base_model']
+    commit_message = f"AutoGPTQ model for {pretrained_model_dir}: {quantize_config.bits}bits, gr{quantize_config.group_size}, desc_act={quantize_config.desc_act}"
+    # model.push_to_hub(repo_id, commit_message=commit_message, use_auth_token=True, use_safetensors=True, safe_serialization=True)
+    # model.push_to_hub(repo_id, commit_message=commit_message, use_auth_token=True, safe_serialization=True)
+    model.push_to_hub(repo_id, commit_message=commit_message, use_auth_token=True, use_safetensors=True)
+    tokenizer.push_to_hub(repo_id, commit_message=commit_message, use_auth_token=True)
+    print("Pushed.")
+
+# def push_tokenizer(tokenizer):
+
+def get_quantized_model_id(cfg: DictDefault):
+# def get_quantized_model_id(cfg: DictDefault, quantize_config):
+    # return f"{cfg.hub_model_id}-{quantize_config.bits}bits-gr{quantize_config.group_size}-desc_act{quantize_config.desc_act}"
+    if not cfg.hub_model_id:
+        raise ValueError("Missing hub_model_id in the configuration.")
+    return f"{cfg.hub_model_id}-GPTQ"
+
+def get_quantized_model_dir(cfg: DictDefault):
+# def get_quantized_model_dir(cfg: DictDefault, quantize_config):
+    if not cfg.output_dir:
+        raise ValueError("Missing output_dir in the configuration.")
+    return f"{cfg.output_dir.lstrip('./')}-GPTQ"
+
+def main():
+    print("Starting...")
+    # return
+    # prompt = "<|prompt|>How can entrepreneurs start building their own communities even before launching their product?</s><|answer|>"
+
+    should_quantize = False
+    # tokenizer = get_tokenizer()
+
+    cfg = load_cfg(config_path)
+
+    cfg['lora_model_dir'] = cfg['output_dir']
+
+    LOG.info(f"loading tokenizer... {cfg.tokenizer_config or cfg.base_model_config}")
+    tokenizer = load_tokenizer(cfg)
+
+    if should_quantize:
+        print("Quantizing...")
+
+        print("Loading dataset...")
+        datasets = load_datasets(cfg=cfg, cli_args=TrainerCliArgs())
+        train_dataset = datasets.train_dataset
+        n_samples = 128
+        # n_samples = 2
+        examples = train_dataset.shuffle(seed=42).select(
+                [
+                    random.randrange(0, len(train_dataset) - 1)  # nosec
+                    for _ in range(n_samples)
+                ]
+            )
+
+        LOG.info("loading model and (optionally) peft_config...")
+        # model, peft_config = load_model(cfg, tokenizer, inference=True)
+        model = load_merged_model(cfg)
+        # model = get_model()
+
+        # examples = load_data(dataset_name, tokenizer, n_samples)
+
+        # print(examples)
+        examples_for_quant = [
+            {"input_ids": example["input_ids"], "attention_mask": example["attention_mask"]}
+            for example in examples
+        ]
+        # print(examples_for_quant)
+
+        modelq = quantize_and_save(cfg, model, tokenizer, examples_for_quant)
+    else:
+        print("Loading quantized model...")
+        modelq = get_quantized_model(cfg)
+
+    push_model(cfg, modelq, tokenizer)
+
+main()
+
+
+# Load configure
+# Load dataset
+# Load tokenizer
+# Prepare database
+# Load previous model, final checkpoint
+
+
+# --merge_lora --lora_model_dir="./completed-model" --load_in_8bit=False --load_in_4bit=False
+# accelerate launch ./scripts/finetune.py ./examples/llama-2/lora.yml --merge_lora --lora_model_dir="./lora-out" --load_in_8bit=False --load_in_4bit=False
+# CUDA_VISIBLE_DEVICES="1" accelerate launch ./scripts/finetune.py ./examples/llama-2/lora.yml --merge_lora --lora_model_dir="./lora-out" --load_in_8bit=False --load_in_4bit=False
+
+# HUB_MODEL_ID="Glavin001/llama-2-7b-alpaca_2k_test" accelerate launch ./scripts/quantize.py
+
diff --git a/src/axolotl/utils/config.py b/src/axolotl/utils/config.py
index abb3154d21..c65709f59d 100644
--- a/src/axolotl/utils/config.py
+++ b/src/axolotl/utils/config.py
@@ -71,6 +71,11 @@ def normalize_config(cfg):
 
     log_gpu_memory_usage(LOG, "baseline", cfg.device)
 
+    if os.environ.get("WANDB_PROJECT") and len(os.environ.get("WANDB_PROJECT", "")) > 0:
+        cfg.wandb_project = os.environ.get("WANDB_PROJECT")
+
+    if os.environ.get("HUB_MODEL_ID") and len(os.environ.get("HUB_MODEL_ID", "")) > 0:
+        cfg.hub_model_id = os.environ.get("HUB_MODEL_ID")
 
 def validate_config(cfg):
     if cfg.max_packed_sequence_len and cfg.sample_packing: