Remove doc in this PR

Signed-off-by: Angel Luu <[email protected]>

README.md

@@ -9,7 +9,6 @@
   - [Tips on Parameters to Set](#tips-on-parameters-to-set)
 - [Tuning Techniques](#tuning-techniques)
   - [LoRA Tuning Example](#lora-tuning-example)
-  - [qLoRA Tuning Example](#lora-tuning-example)
   - [Prompt Tuning](#prompt-tuning)
   - [Fine Tuning](#fine-tuning)
   - [FMS Acceleration](#fms-acceleration)
@@ -433,73 +432,6 @@ Example 3:
 
 _________________________
 
-
-### qLoRA Tuning Example
-
-This method is similar to LoRA Tuning, but the base model is a quantized model.
-Set `peft_method` to `"lora"`. You can pass any of LoraConfig, see section on [LoRA Example](#lora-tuning-example).
-In addition, you can pass [LoRA quantization config](https://github.com/foundation-model-stack/fms-hf-tuning/blob/main/tuning/config/acceleration_configs/quantized_lora_config.py#L62).
-```py
-# to use auto_gptq 4bit lora base layers
-auto_gptq: AutoGPTQLoraConfig = None
-
-# to use auto_gptq 4bit lora base layers
-bnb_qlora: BNBQLoraConfig = None
-```
-
-```py
-class AutoGPTQLoraConfig:
-
-  # auto_gptq supports various kernels, to select the kernel to use.
-  kernel: str = "triton_v2"
-
-  # allow auto_gptq to quantize a model before training commences.
-  # NOTE: currently this is not allowed.
-  from_quantized: bool = True
-
-```
-
-Example command to run:
-
-```bash
-python tuning/sft_trainer.py \
---model_name_or_path $MODEL_PATH \
---tokenizer_name_or_path $MODEL_PATH \ # This field is optional and if not specified, tokenizer from model_name_or_path will be used
---training_data_path $TRAIN_DATA_PATH \
---output_dir $OUTPUT_PATH \
---num_train_epochs 40 \
---per_device_train_batch_size 4 \
----learning_rate 1e-4 \
---response_template "\n### Label:" \
---dataset_text_field "output" \
---peft_method "lora" \
---r 8 \
---lora_dropout 0.05 \
---lora_alpha 16 \
---target_modules c_attn c_proj
---auto_gptq triton_v2
-
-Equally you can pass in a JSON configuration for running tuning. See [build doc](./build/README.md) for more details. The above can also be passed in as JSON:
-```json
-{
-    "model_name_or_path": $MODEL_PATH,
-    "training_data_path": $TRAIN_DATA_PATH,
-    "output_dir": $OUTPUT_PATH,
-    "num_train_epochs": 40.0,
-    "per_device_train_batch_size": 4,
-    "learning_rate": 1e-4,
-    "response_template": "\n### Label:",
-    "dataset_text_field": "output",
-    "peft_method": "lora",
-    "r": 8,
-    "lora_dropout": 0.05,
-    "lora_alpha": 16,
-    "target_modules": ["c_attn", "c_proj"]
-    "auto_gptq": ["triton_v2"]
-}
-```
-_________________________
-
 ### Prompt Tuning:
 
 Specify `peft_method` to `'pt'` . You can additionally pass any arguments from [PromptTuningConfig](https://github.com/foundation-model-stack/fms-hf-tuning/blob/main/tuning/config/peft_config.py#L63).