feat(docs): improve user customized prompts (#443)

* feat(docs): improve user customized prompts

* feat(doc): add custom pretokenized instructions

* chore: clean old data folder

* chore: add new line

README.md

@@ -16,6 +16,7 @@ Axolotl is a tool designed to streamline the fine-tuning of various AI models, o
   - [LambdaLabs Installation](#lambdalabs)
 - [Dataset](#dataset)
   - [How to Add Custom Prompts](#how-to-add-custom-prompts)
+  - [How to Use Custom Pretokenized Dataset](#how-to-use-your-custom-pretokenized-dataset)
 - [Config](#config)
   - [Train](#train)
   - [Inference](#inference)
@@ -99,7 +100,7 @@ accelerate launch scripts/finetune.py examples/openllama-3b/lora.yml \
   ```
 
 - Conda/Pip venv
-  1. Install python **3.9**
+  1. Install python >=**3.9**
 
   2. Install pytorch stable https://pytorch.org/get-started/locally/
 
@@ -273,11 +274,29 @@ Have dataset(s) in one of the following format (JSONL recommended):
 
 #### How to add custom prompts
 
-  1. Add your method to a file in [prompt_strategies](src/axolotl/prompt_strategies). Please see other files as example.
+Using yaml. Example:
-  2. Use your custom file name as the dataset type `<prompt_strategies_file>.load_<load_fn>`.
+```yaml
+datasets:
+  - path: repo
+    type:
+      system_prompt: ""
+      no_input_format: |-
+        User: {instruction}<|end_of_turn|>
+        Assistant:
+      format: |-
+        User: {instruction}
+        {input}<|end_of_turn|>
+        Assistant:
+```
 
-Optionally, download some datasets, see [data/README.md](data/README.md)
+Using file:
+1. Add your method to a file in [prompt_strategies](src/axolotl/prompt_strategies). Please see other files as example.
+2. Use your custom file name as the dataset type `<prompt_strategies_file>.load_<load_fn>`.
 
+#### How to use your custom pretokenized dataset
+
+- Do not pass a `type:`
+- Dataset must contain `input_ids`, `attention_mask`, `labels` in columns
 
 
 ### Config
@@ -307,9 +326,9 @@ See [examples](examples) for quick start. It is recommended to duplicate and mod
 
   # local
   datasets:
-    - path: json
+    - path: data.jsonl # or json
-      data_files: data.jsonl # or json
+      ds_type: json # see other options below
-      type: alpaca # format from earlier
+      type: alpaca
   ```
 
 - loading
@@ -395,6 +414,24 @@ datasets:
     shards: # number of shards to split data into
     name: # name of dataset configuration to load
 
+  # custom user prompt
+  - path: repo
+    type:
+      # the below are defaults. only set what's needed.
+      system_prompt: ""
+      field_system: system
+      field_instruction: instruction
+      field_output: input
+
+      # customizable to be single line or multi-line
+      system_format: "{system}"
+      # 'format' can include {input}
+      format: |-
+        User: {instruction} {input}
+        Assistant:
+      # 'no_input_format' cannot include {input}
+      no_input_format: "{instruction} "
+
 # axolotl attempts to save the dataset as an arrow after packing the data together so
 # subsequent training attempts load faster, relative path
 dataset_prepared_path: data/last_run_prepared
@@ -667,7 +704,9 @@ Please reduce any below
   - `gradient_accumulation_steps`
   - `sequence_len`
 
-> `failed (exitcode: -9)` usually means your system has run out of system memory.
+> `failed (exitcode: -9)`
+
+Usually means your system has run out of system memory.
 Similarly, you should consider reducing the same settings as when you run out of VRAM.
 Additionally, look into upgrading your system RAM which should be simpler than GPU upgrades.
 

data/README.md

@@ -1,24 +0,0 @@
-
-## Download some datasets
-```shell
-curl https://raw.githubusercontent.com/tloen/alpaca-lora/main/alpaca_data_gpt4.json -o data/raw/alpaca_data_gpt4.json
-curl https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json -L -o data/raw/vicuna_cleaned.json
-curl https://github.com/teknium1/GPTeacher/blob/main/Instruct/gpt4-instruct-similarity-0.6-dataset.json?raw=true -L -o data/raw/gpt4-instruct-similarity-0.6-dataset.json
-curl https://github.com/teknium1/GPTeacher/blob/main/Roleplay/roleplay-similarity_0.6-instruct-dataset.json?raw=true -L -o data/raw/roleplay-similarity_0.6-instruct-dataset.json
-```
-
-## Convert the JSON data files to JSONL.
-
-```shell
-python3 ./scripts/alpaca_json_to_jsonl.py --file data/alpaca_data_gpt4.json --output data/alpaca_data_gpt4.jsonl
-python3 ./scripts/alpaca_json_to_jsonl.py --file data/raw/vicuna_cleaned.json --output data/vicuna_cleaned.jsonl
-python3 ./scripts/alpaca_json_to_jsonl.py --file data/raw/roleplay-similarity_0.6-instruct-dataset.json --output data/roleplay-similarity_0.6-instruct-dataset.jsonl
-python3 ./scripts/alpaca_json_to_jsonl.py --file data/raw/gpt4-instruct-similarity-0.6-dataset.json --output data/gpt4-instruct-similarity-0.6-dataset.jsonl
-```
----
-
-Using JSONL makes it easier to subset the data if you want a smaller training set, i.e get 2000 random examples.
-
-```shell
-shuf -n2000 data/vicuna_cleaned.jsonl > data/vicuna_cleaned.subset0.jsonl
-```

data/raw/.gitignore

@@ -1 +0,0 @@
-**

scripts/alpaca_json_to_jsonl.py

@@ -1,52 +0,0 @@
-"""Module to convert json file to jsonl"""
-
-import os
-import sys
-from pathlib import Path
-from typing import Optional, Union
-
-import fire
-
-from axolotl.convert import (
-    FileReader,
-    FileWriter,
-    JsonlSerializer,
-    JsonParser,
-    JsonToJsonlConverter,
-    StdoutWriter,
-)
-from axolotl.logging_config import configure_logging
-
-configure_logging()
-
-# add src to the pythonpath so we don't need to pip install this
-project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
-src_dir = os.path.join(project_root, "src")
-sys.path.insert(0, src_dir)
-
-
-def main(
-    file: Path,
-    output: Optional[Path] = None,
-    to_stdout: Optional[bool] = False,
-):
-    """
-    Convert a json file to jsonl
-    """
-
-    file_reader = FileReader()
-    writer: Union[StdoutWriter, FileWriter]
-    if to_stdout or output is None:
-        writer = StdoutWriter()
-    else:
-        writer = FileWriter(output)
-    json_parser = JsonParser()
-    jsonl_serializer = JsonlSerializer()
-
-    converter = JsonToJsonlConverter(file_reader, writer, json_parser, jsonl_serializer)
-
-    converter.convert(file, output)
-
-
-if __name__ == "__main__":
-    fire.Fire(main)