From c3e1882de559a9b1c92037b7bad81f38aba1e731 Mon Sep 17 00:00:00 2001 From: Dan Saunders Date: Fri, 22 Aug 2025 02:43:16 -0400 Subject: [PATCH] progress --- .../colab-axolotl-example.ipynb | 19861 ++++++++-------- requirements.txt | 1 + .../prompt_strategies/messages/chat.py | 7 +- src/axolotl/tui/app.py | 8 +- src/axolotl/tui/dialogs/training.py | 2 +- src/axolotl/tui/screens/config.py | 11 +- src/axolotl/tui/screens/datasets.py | 113 +- src/axolotl/tui/screens/inference.py | 106 +- src/axolotl/tui/screens/models.py | 82 +- src/axolotl/tui/screens/monitor.py | 42 +- src/axolotl/tui/screens/training.py | 35 +- 11 files changed, 10087 insertions(+), 10181 deletions(-) diff --git a/examples/colab-notebooks/colab-axolotl-example.ipynb b/examples/colab-notebooks/colab-axolotl-example.ipynb index 69881997e..80686f423 100644 --- a/examples/colab-notebooks/colab-axolotl-example.ipynb +++ b/examples/colab-notebooks/colab-axolotl-example.ipynb @@ -1,9934 +1,9933 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "OPLSwmgdrB7g" - }, - "source": [ - "# Fine-Tune Qwen3 14B with Axolotl\n", - "\n", - "[\"Built](https://github.com/axolotl-ai-cloud/axolotl)\n", - "\n", - "Axolotl is the most performant LLM post-training framework available, delivering faster training with efficient, consistent and stable performance. Train your workload and ship your product 30% faster; saving you both time and money.\n", - "\n", - "- ⭐ us on [GitHub](https://github.com/axolotl-ai-cloud/axolotl)\n", - "- πŸ“œ Read the [Docs](http://docs.axolotl.ai/)\n", - "- πŸ’¬ Chat with us on [Discord](https://discord.gg/mnpEYgRUmD)\n", - "- πŸ“° Get updates on [X/Twitter](https://x.com/axolotl_ai)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "rVjKD7CbxIP3" - }, - "source": [ - "# Installation\n", - "\n", - "Axolotl is easy to install from [pip](https://pypi.org/project/axolotl/), or use our [pre-built Docker images](http://docs.axolotl.ai/docs/docker.html) for a hassle free dependency experience. See our [docs](http://docs.axolotl.ai/docs/installation.html) for more information." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "msOCO4NRmRLa" - }, - "outputs": [], - "source": [ - "%%capture\n", - "# This step can take ~5-10 minutes to install dependencies\n", - "!pip install --no-build-isolation axolotl[flash-attn]>=0.9.1\n", - "!pip install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@0ee9ee8\"" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "N0OW0YeksDLr" - }, - "source": [ - "## Demo: Talk Like a Pirate\n", - "\n", - "In this demo, we are training the model ***to respond like a pirate***. This was chosen as a way to easily show how to train a model to respond in a certain style of your choosing (without being prompted) and is quite easy to validate within the scope of a Colab." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "8Du2fANTsNCK" - }, - "source": [ - "### Upload your own dataset or use a Huggingface dataset\n", - "\n", - "You can choose to use your own JSONL file from your own [Google Drive](https://drive.google.com/drive/home); for example downloading the [Pirate-Ultrachat JSONL](https://huggingface.co/datasets/winglian/pirate-ultrachat-10k/blob/main/train.jsonl) to your Google Drive. JSONL datasets should be formatted similar to the [OpenAI dataset format](https://cookbook.openai.com/examples/chat_finetuning_data_prep).\n", - "\n", - "You can also simply use the [`winglian/pirate-ultrachat-10k`](https://huggingface.co/datasets/winglian/pirate-ultrachat-10k) dataset directly.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "fGEEjyQ-r_IV" - }, - "outputs": [], - "source": [ - "# Default to HF dataset location\n", - "dataset_id = \"winglian/pirate-ultrachat-10k\"\n", - "uploaded = {}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "c5MyYqk7vIsG" - }, - "outputs": [], - "source": [ - "import os\n", - "# Optionally, upload your own JSONL to your Google Drive\n", - "GOOGLE_DRIVE_PATH = \"\" # ex: \"MyDrive/Colab\\ Notebooks/train.jsonl\"\n", - "\n", - "# \"Select All\" permissions, or you may get the error:\n", - "# \"MessageError: Error: credential propagation was unsuccessful\"\n", - "if GOOGLE_DRIVE_PATH:\n", - " from google.colab import drive\n", - " # Mount your Google Drive\n", - " GOOGLE_DRIVE_MNT = \"/content/drive/\"\n", - " drive.mount(GOOGLE_DRIVE_MNT, force_remount=True)\n", - " tmp_path = os.path.join(GOOGLE_DRIVE_MNT, GOOGLE_DRIVE_PATH.lstrip(\"/\"))\n", - " # make sure file exists\n", - " if not os.path.isfile(tmp_path):\n", - " raise ValueError(f\"File {tmp_path} does not exist\")\n", - " dataset_id = tmp_path\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "U6pTk3A9xj1W" - }, - "source": [ - "# Configure for Supervised Fine-Tuning (SFT)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 151, - "referenced_widgets": [ - "388f618924274d21a066f098f4f1e744", - "7c95f85a2b1f47a1bd846d110c47bb3c", - "083f9cda8d754c168beee10d2f8955a2", - "62e1a65582f446a78612eaa804e08a7d", - "487a177d020f4605834878b2fdc7afa3", - "7fd44cf9ca6e4726bfd7ac21846d6a14", - "366a343b62fa47d8985a3bd464d99f9e", - "a0a11e929edd4189b79723d618522c33", - "e87ea87fcff247b5bbcc331ba79a8dc2", - "5e18768f7ad6434ba8b8b8a2e853e204", - "bb33aec33a6447078c31bfd728942994" - ] - }, - "id": "fdRioqytmTtX", - "outputId": "f0acdcec-4b41-4a3f-ffed-c2d2d929158e" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2025-05-08 13:40:27,488] [INFO] [root.register:348] [PID:174] Attempting to load plugin: axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin\n", - "[2025-05-08 13:40:27,493] [INFO] [root.register:351] [PID:174] Plugin loaded successfully: axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin\n", - "[2025-05-08 13:40:27,959] [INFO] [axolotl.utils.schemas.config.check_eval_packing:721] [PID:174] [RANK:0] explicitly setting `eval_sample_packing` to match `sample_packing`\u001b[39m\n", - "[2025-05-08 13:40:27,960] [INFO] [axolotl.utils.schemas.config.hint_sample_packing_padding:514] [PID:174] [RANK:0] Setting `pad_to_sequence_len: true` to prevent memory leaks when sample_packing\u001b[39m\n", - "[2025-05-08 13:40:27,961] [INFO] [axolotl.utils.schemas.config.check_bf16:1251] [PID:174] [RANK:0] bf16 support detected, but not enabled for this configuration.\u001b[39m\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "388f618924274d21a066f098f4f1e744", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "config.json: 0%| | 0.00/728 [00:00\"],\n", - " }\n", - " ],\n", - " dataloader_prefetch_factor = 8, # dataloader optimizations\n", - " dataloader_num_workers = 2,\n", - " dataloader_pin_memory = True,\n", - " )\n", - "\n", - "# validates the configuration\n", - "cfg = load_cfg(config)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "715UpvnSoBIS" - }, - "outputs": [], - "source": [ - "from axolotl.utils import patch_optimized_env\n", - "# speedup downloads from HF πŸ€— and set \"PYTORCH_CUDA_ALLOC_CONF\" env to save memory\n", - "patch_optimized_env()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Vc6MC-hwyH-n" - }, - "source": [ - "# Datasets\n", - "\n", - "Axolotl has a robust suite of loaders and transforms to parse most open datasets of any format into the appropriate chat template for your model. Axolotl will mask input tokens from the user's prompt so that the train loss is only calculated against the model's response. For more information, [see our documentation](http://docs.axolotl.ai/docs/dataset-formats/conversation.html) on dataset preparation.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000, - "referenced_widgets": [ - "b82aa8c57f7c422a9a9c90f333ed2a99", - "c0991cf63ee6458b96e9a75e7a88b61a", - "71c8af139cd248b1b51101fd46a93f35", - "1d5117195d4b49eb8f1a73b18419f7ce", - "3c21e4a511b4441192c03b7f1d0976e9", - "ed28e2e0410d4e0b855467e798e53d66", - "d93f134f802b4b69b575bdaf07dbd27c", - "d0e9dce55cec4c1ca619a0ccf209d924", - "4c727d40ef0443449afc31724ee79f0c", - "0dea5caa27384f5689e3cab51f558727", - "a6f48410b9964fefba0c3009a77dc838", - "95caff42f08a4c2aa14c867b8f37f231", - "de7c37ee83e24f0c889e84d07279c2ec", - "9d4897eefb5f48259ffb2d23e332f752", - "253017b0d0534e54ab44e181f6d7c82d", - "27beaf06e41b472abdb544a43c720c5a", - "34cf3df51fbc41cabfdbba153c007f0e", - "ac764024cf1c4e08ba7749afd2cd20ac", - "30a81da86f8043eca301e86a8651201a", - "e8b7a81040904c1e89e58978223b1737", - "1c6f1f10667545aaab958016ba7e2c94", - "e6e969610738449887259063967f82b0", - "a138859f19b74fc0928dc236ab5359db", - "9b42e08b3c9548818488268768a118b1", - "12b56912736849fea2ad8124456fdc5c", - "879c8ab5873847a8833bd74123be90a4", - "20352e5f58d24bb8b1f3940efd14fe4a", - "d955dcaa0e944e719f3a06139dd54a03", - "d3de2662c7964f1ba96e58da382af720", - "97e36007e1304e1583fd81bfb13f0edd", - "c65dc74c7d6f4bab8f7dd28455161dd8", - "ef223e8504b64e3592589880326aaf41", - "598da69727bd4fb8b1caf465ac736d7a", - "5f86cd894de94c3280fadc1e2fd0ee13", - "a20927bf5f2c41f58c1e31ac858ab36c", - "0a46ad75c198463d843fb35e813642cb", - "09007681cf8d42aeb8c1d2f6a74e470a", - "ebc80d1a55fa47f4a5ea2756588569ec", - "1811cda0644e4190a9469d1774435d82", - "35c811d2ae8e43f3b5cecbdd3cfa857f", - "b8e39e4dddc3497fbc29ae45c66da759", - "63b4e563e85c4f03b1b72beda9577bcc", - "b195f160ca20442fadd8b5aed0ee41af", - "ca65e32eb52f48c09a84b33cb18f22cd", - "7cd0b85ebd204b7aba908417811ce4e0", - "7baeab52d6694c32b1efd1ea1a0a7782", - 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"version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "tokenizer_config.json: 0%| | 0.00/9.68k [00:00\u001b[39m\n", - "[2025-05-08 13:41:00,845] [DEBUG] [axolotl.utils.models.load_tokenizer:442] [PID:174] [RANK:0] BOS: None / None\u001b[39m\n", - "[2025-05-08 13:41:00,846] [DEBUG] [axolotl.utils.models.load_tokenizer:443] [PID:174] [RANK:0] PAD: 151643 / <|endoftext|>\u001b[39m\n", - "[2025-05-08 13:41:00,847] [DEBUG] [axolotl.utils.models.load_tokenizer:444] [PID:174] [RANK:0] UNK: None / None\u001b[39m\n", - "[2025-05-08 13:41:00,869] [INFO] [axolotl.utils.data.sft.load_tokenized_prepared_datasets:271] [PID:174] [RANK:0] Unable to find prepared dataset in last_run_prepared/97037817611d38b3a9c681753c3c4c95\u001b[39m\n", - "[2025-05-08 13:41:00,870] [INFO] [axolotl.utils.data.sft.load_tokenized_prepared_datasets:272] [PID:174] [RANK:0] Loading raw datasets...\u001b[39m\n", - "\u001b[33m[2025-05-08 13:41:00,870] [WARNING] [axolotl.utils.data.sft.load_tokenized_prepared_datasets:274] [PID:174] [RANK:0] Processing datasets during training can lead to VRAM instability. Please pre-process your dataset.\u001b[39m\n", - "[2025-05-08 13:41:00,871] [INFO] [axolotl.utils.data.sft.load_tokenized_prepared_datasets:281] [PID:174] [RANK:0] No seed provided, using default seed of 42\u001b[39m\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "7cd0b85ebd204b7aba908417811ce4e0", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "train.jsonl: 0%| | 0.00/27.3M [00:00system\\n' }}\n", - " {%- if messages[0].role == 'system' %}\n", - " {{- messages[0].content + '\\n\\n' }}\n", - " {%- endif %}\n", - " {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n", - " {%- for tool in tools %}\n", - " {{- \"\\n\" }}\n", - " {{- tool | tojson }}\n", - " {%- endfor %}\n", - " {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}\n", - "{%- else %}\n", - " {%- if messages[0].role == 'system' %}\n", - " {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n", - " {%- endif %}\n", - "{%- endif %}\n", - "{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n", - "{%- for message in messages[::-1] %}\n", - " {%- set index = (messages|length - 1) - loop.index0 %}\n", - " {%- if ns.multi_step_tool and message.role == \"user\" and not(message.content.startswith('') and message.content.endswith('')) %}\n", - " {%- set ns.multi_step_tool = false %}\n", - " {%- set ns.last_query_index = index %}\n", - " {%- endif %}\n", - "{%- endfor %}\n", - "{%- for message in messages %}\n", - " {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n", - " {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n", - " {%- elif message.role == \"assistant\" %}\n", - " {%- set content = message.content %}\n", - " {%- set reasoning_content = '' %}\n", - " {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n", - " {%- set reasoning_content = message.reasoning_content %}\n", - " {%- else %}\n", - " {%- if '' in message.content %}\n", - " {%- set content = message.content.split('')[-1].lstrip('\\n') %}\n", - " {%- set reasoning_content = message.content.split('')[0].rstrip('\\n').split('')[-1].lstrip('\\n') %}\n", - " {%- endif %}\n", - " {%- endif %}\n", - " {%- if loop.index0 > ns.last_query_index %}\n", - " {%- if loop.last or (not loop.last and reasoning_content) %}\n", - " {{- '<|im_start|>' + message.role + '\\n\\n' + reasoning_content.strip('\\n') + '\\n\\n\\n' + content.lstrip('\\n') }}\n", - " {%- else %}\n", - " {{- '<|im_start|>' + message.role + '\\n' + content }}\n", - " {%- endif %}\n", - " {%- else %}\n", - " {{- '<|im_start|>' + message.role + '\\n' + content }}\n", - " {%- endif %}\n", - " {%- if message.tool_calls %}\n", - " {%- for tool_call in message.tool_calls %}\n", - " {%- if (loop.first and content) or (not loop.first) %}\n", - " {{- '\\n' }}\n", - " {%- endif %}\n", - " {%- if tool_call.function %}\n", - " {%- set tool_call = tool_call.function %}\n", - " {%- endif %}\n", - " {{- '\\n{\"name\": \"' }}\n", - " {{- tool_call.name }}\n", - " {{- '\", \"arguments\": ' }}\n", - " {%- if tool_call.arguments is string %}\n", - " {{- tool_call.arguments }}\n", - " {%- else %}\n", - " {{- tool_call.arguments | tojson }}\n", - " {%- endif %}\n", - " {{- '}\\n' }}\n", - " {%- endfor %}\n", - " {%- endif %}\n", - " {{- '<|im_end|>\\n' }}\n", - " {%- elif message.role == \"tool\" %}\n", - " {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n", - " {{- '<|im_start|>user' }}\n", - " {%- endif %}\n", - " {{- '\\n\\n' }}\n", - " {{- message.content }}\n", - " {{- '\\n' }}\n", - " {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n", - " {{- '<|im_end|>\\n' }}\n", - " {%- endif %}\n", - " {%- endif %}\n", - "{%- endfor %}\n", - "{%- if add_generation_prompt %}\n", - " {{- '<|im_start|>assistant\\n' }}\n", - " {%- if enable_thinking is defined and enable_thinking is false %}\n", - " {{- '\\n\\n\\n\\n' }}\n", - " {%- endif %}\n", - "{%- endif %}\n", - "---\u001b[39m\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "258b7c635c1045329d4669e48c46ccd5", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Tokenizing Prompts (num_proc=2): 0%| | 0/9985 [00:00\n", - " \n", - " \n", - " [25/25 09:25, Epoch 0/1]\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
StepTraining Loss
11.092300
21.554200
31.041400
41.733800
51.430000
61.258500
71.343600
81.101700
91.086500
100.813200
110.689600
120.826700
131.541800
140.948000
151.357000
161.085800
171.516800
181.146800
190.834800
200.968000
211.388800
221.511500
231.338500
241.206600
251.504600

" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2025-05-07 22:12:42,746] [INFO] [axolotl.callbacks.on_step_end:128] [PID:1336] [RANK:0] cuda memory usage while training: 9.768GB (+3.287GB cache, +0.646GB misc)\u001b[39m\n", - "[2025-05-07 22:21:46,859] [INFO] [axolotl.train.save_trained_model:231] [PID:1336] [RANK:0] Training completed! Saving pre-trained model to ./outputs/qwen-sft-pirate-rrr.\u001b[39m\n" - ] - } - ], - "source": [ - "from axolotl.train import train\n", - "\n", - "# just train the first 25 steps for demo.\n", - "# This is sufficient to align the model as we've used packing to maximize the trainable samples per step.\n", - "cfg.max_steps = 25\n", - "model, tokenizer, trainer = train(cfg=cfg, dataset_meta=dataset_meta)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "j1b9ypF78eCb" - }, - "source": [ - "# Inferencing the trained model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "r3_vHhif8YEs", - "outputId": "e5050605-f6c9-421c-98f9-bde56a281eae" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ahoy there, matey! Shiver me timbers, ye be lookin' for the Pythagorean theorem, eh? Well, hold yer horses and listen up, for I'll be tellin' ye all about it in me own special way.\n", - "\n", - "The Pythagorean theorem be a real gem of a mathematical trick that helps ye find the length of a side of a right triangle. Now, a right triangle be a triangle with a right angle, which be that little corner that looks like a square. \n", - "\n", - "The theorem be named after a clever fellow named Pythagoras, who be a mathematician from ancient Greece. He discovered that if ye have a right triangle, the square of the length of the hypotenuse (that be the side opposite the right angle) be equal to the sum of the squares of the other two sides. \n", - "\n", - "In other words, if ye have a triangle with sides of length a, b, and c (\n" - ] - } - ], - "source": [ - "import torch\n", - "from transformers import TextStreamer\n", - "\n", - "messages = [\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": \"Explain the Pythagorean theorem to me.\",\n", - " },\n", - "]\n", - "\n", - "prompt = tokenizer.apply_chat_template(\n", - " messages,\n", - " add_generation_prompt=True,\n", - " tokenize=False,\n", - " enable_thinking = False,\n", - ")\n", - "\n", - "outputs = model.generate(\n", - " **tokenizer(prompt, return_tensors = \"pt\").to(\"cuda\"),\n", - " max_new_tokens = 192,\n", - " temperature = 1.0, top_p = 0.8, top_k = 32,\n", - " streamer = TextStreamer(tokenizer, skip_prompt = True),\n", - ")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "HoGwT2JRSIjA" - }, - "source": [ - "# Saving your trained model\n", - "\n", - "Axolotl automatically saves checkpoints to the `output_dir` path.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "5BmSbiy6NaaS", - "outputId": "f5e1d913-7d55-42d2-8340-f9f1b0bc2b38" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total 506M\n", - "-rw-r--r-- 1 root root 845 May 7 22:21 adapter_config.json\n", - "-rw-r--r-- 1 root root 491M May 7 22:21 adapter_model.safetensors\n", - "-rw-r--r-- 1 root root 707 May 7 22:11 added_tokens.json\n", - "drwxr-xr-x 2 root root 4.0K May 7 22:17 checkpoint-13\n", - "drwxr-xr-x 2 root root 4.0K May 7 22:21 checkpoint-25\n", - "-rw-r--r-- 1 root root 1.2K May 7 22:11 config.json\n", - "-rw-r--r-- 1 root root 1.6M May 7 22:11 merges.txt\n", - "-rw-r--r-- 1 root root 2.6K May 7 22:21 README.md\n", - "-rw-r--r-- 1 root root 613 May 7 22:11 special_tokens_map.json\n", - "-rw-r--r-- 1 root root 9.5K May 7 22:11 tokenizer_config.json\n", - "-rw-r--r-- 1 root root 11M May 7 22:11 tokenizer.json\n", - "-rw-r--r-- 1 root root 2.7M May 7 22:11 vocab.json\n" - ] - } - ], - "source": [ - "# Show the saved checkpoints in the output_dir\n", - "!ls -lh \"./outputs/qwen-sft-pirate-rrr\"" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "_PCIFWxuOZd6" - }, - "source": [ - "Setting `hub_model_id: ` in the original config would have automatically uploaded the model to HuggingFace Hub (e.g. `hub_model_id: username/model_id`)\n", - "\n", - "If you prefer to manually upload the training artifacts, we can still upload the entire final checkpoint to HuggingFace from the CLI." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 955, - "referenced_widgets": [ - "c12ea43372ac4d57bb9605f1a429b397", - "86816687746246b4a6105e8010384e25", - "6f05e9bebf7b40c9835808e77de6c236", - "c7433acd3c4841e6958ae8f7e87b1808", - "19c1e38389fa46c7b7e2152a56e1df34", - "0e067d8db8ed48308a718d5f57683fd1", - "131065f118274a1586ac38e39ed84ef0", - "8640ac440fbc4644b9a3af7ba3ae7183", - "5cea7996f02040b187ece0bb2d6a8d1f", - "2e257c8be2da40b4bb67a9e4ab6811f3", - "56e3768bef5a4b9db4168c5c17f509c2", - "62c028fdef904dedb9cdeca2b3bda725", - "a7cf477e80fc43e0ad82c7997b076dce", - "835bcc28a5564fb9b3d651bc8e32dc46", - "9f1c9a0695384bdaa6f8b847ef89bee8", - "b1bea589efa14258a9982071b87938bf", - "590eef89881545aa8bbef9a8bbe7fb00", - "4b1f04ff63d14a118fdd15814dff50e4", - "39789237703c4a418134243055c9cbf5", - "a3a945817f684328b34651fe052393ec" - ] - }, - "id": "2yw8pLvlSMl8", - "outputId": "6e489ab2-4abe-4e28-84ca-959f912433a4" - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c12ea43372ac4d57bb9605f1a429b397", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(HTML(value='

\n", - " sys.exit(main())\n", - " ^^^^^^\n", - " File \"/usr/local/lib/python3.11/dist-packages/huggingface_hub/commands/huggingface_cli.py\", line 57, in main\n", - " service.run()\n", - " File \"/usr/local/lib/python3.11/dist-packages/huggingface_hub/commands/upload.py\", line 207, in run\n", - " print(self._upload())\n", - " ^^^^^^^^^^^^^^\n", - " File \"/usr/local/lib/python3.11/dist-packages/huggingface_hub/commands/upload.py\", line 302, in _upload\n", - " return self.api.upload_folder(\n", - " ^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_validators.py\", line 114, in _inner_fn\n", - " return fn(*args, **kwargs)\n", - " ^^^^^^^^^^^^^^^^^^^\n", - " File \"/usr/local/lib/python3.11/dist-packages/huggingface_hub/hf_api.py\", line 1633, in _inner\n", - " return fn(self, *args, **kwargs)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/usr/local/lib/python3.11/dist-packages/huggingface_hub/hf_api.py\", line 4942, in upload_folder\n", - " commit_info = self.create_commit(\n", - " ^^^^^^^^^^^^^^^^^^^\n", - " File \"/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_validators.py\", line 114, in _inner_fn\n", - " return fn(*args, **kwargs)\n", - " ^^^^^^^^^^^^^^^^^^^\n", - " File \"/usr/local/lib/python3.11/dist-packages/huggingface_hub/hf_api.py\", line 1633, in _inner\n", - " return fn(self, *args, **kwargs)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/usr/local/lib/python3.11/dist-packages/huggingface_hub/hf_api.py\", line 4202, in create_commit\n", - " self.preupload_lfs_files(\n", - " File \"/usr/local/lib/python3.11/dist-packages/huggingface_hub/hf_api.py\", line 4483, in preupload_lfs_files\n", - " _upload_xet_files(**upload_kwargs, create_pr=create_pr) # type: ignore [arg-type]\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_validators.py\", line 114, in _inner_fn\n", - " return fn(*args, **kwargs)\n", - " ^^^^^^^^^^^^^^^^^^^\n", - " File \"/usr/local/lib/python3.11/dist-packages/huggingface_hub/_commit_api.py\", line 592, in _upload_xet_files\n", - " with progress_cm as progress:\n", - " File \"/usr/local/lib/python3.11/dist-packages/tqdm/std.py\", line 1138, in __exit__\n", - " def __exit__(self, exc_type, exc_value, traceback):\n", - "\n", - "KeyboardInterrupt\n", - "^C\n" - ] - } - ], - "source": [ - "from huggingface_hub import notebook_login\n", - "# remove the partial epoch checkpoints\n", - "!rm -rf \"./outputs/qwen-sft-pirate-rrr/checkpoint-*\"\n", - "\n", - "# HF Notebook login widget\n", - "notebook_login()\n", - "\n", - "# upload the LoRA adapter for your model to HF, remember to update the username/model-name below\n", - "!huggingface-cli upload --repo-type=model winglian/pirate-qwen-14B \"./outputs/qwen-sft-pirate-rrr\"" - ] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "gpuType": "T4", - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - 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Train your workload and ship your product 30% faster; saving you both time and money.\n", + "\n", + "- ⭐ us on [GitHub](https://github.com/axolotl-ai-cloud/axolotl)\n", + "- πŸ“œ Read the [Docs](http://docs.axolotl.ai/)\n", + "- πŸ’¬ Chat with us on [Discord](https://discord.gg/mnpEYgRUmD)\n", + "- πŸ“° Get updates on [X/Twitter](https://x.com/axolotl_ai)\n" + ] }, - "nbformat": 4, - "nbformat_minor": 0 + { + "cell_type": "markdown", + "metadata": { + "id": "rVjKD7CbxIP3" + }, + "source": [ + "# Installation\n", + "\n", + "Axolotl is easy to install from [pip](https://pypi.org/project/axolotl/), or use our [pre-built Docker images](http://docs.axolotl.ai/docs/docker.html) for a hassle free dependency experience. See our [docs](http://docs.axolotl.ai/docs/installation.html) for more information." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "msOCO4NRmRLa" + }, + "outputs": [], + "source": [ + "%%capture\n", + "# This step can take ~5-10 minutes to install dependencies\n", + "!pip install --no-build-isolation axolotl[flash-attn]>=0.9.1\n", + "!pip install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@0ee9ee8\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "N0OW0YeksDLr" + }, + "source": [ + "## Demo: Talk Like a Pirate\n", + "\n", + "In this demo, we are training the model ***to respond like a pirate***. This was chosen as a way to easily show how to train a model to respond in a certain style of your choosing (without being prompted) and is quite easy to validate within the scope of a Colab." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8Du2fANTsNCK" + }, + "source": [ + "### Upload your own dataset or use a Huggingface dataset\n", + "\n", + "You can choose to use your own JSONL file from your own [Google Drive](https://drive.google.com/drive/home); for example downloading the [Pirate-Ultrachat JSONL](https://huggingface.co/datasets/winglian/pirate-ultrachat-10k/blob/main/train.jsonl) to your Google Drive. JSONL datasets should be formatted similar to the [OpenAI dataset format](https://cookbook.openai.com/examples/chat_finetuning_data_prep).\n", + "\n", + "You can also simply use the [`winglian/pirate-ultrachat-10k`](https://huggingface.co/datasets/winglian/pirate-ultrachat-10k) dataset directly.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "fGEEjyQ-r_IV" + }, + "outputs": [], + "source": [ + "# Default to HF dataset location\n", + "dataset_id = \"winglian/pirate-ultrachat-10k\"\n", + "uploaded = {}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "c5MyYqk7vIsG" + }, + "outputs": [], + "source": [ + "import os\n", + "# Optionally, upload your own JSONL to your Google Drive\n", + "GOOGLE_DRIVE_PATH = \"\" # ex: \"MyDrive/Colab\\ Notebooks/train.jsonl\"\n", + "\n", + "# \"Select All\" permissions, or you may get the error:\n", + "# \"MessageError: Error: credential propagation was unsuccessful\"\n", + "if GOOGLE_DRIVE_PATH:\n", + " from google.colab import drive\n", + " # Mount your Google Drive\n", + " GOOGLE_DRIVE_MNT = \"/content/drive/\"\n", + " drive.mount(GOOGLE_DRIVE_MNT, force_remount=True)\n", + " tmp_path = os.path.join(GOOGLE_DRIVE_MNT, GOOGLE_DRIVE_PATH.lstrip(\"/\"))\n", + " # make sure file exists\n", + " if not os.path.isfile(tmp_path):\n", + " raise ValueError(f\"File {tmp_path} does not exist\")\n", + " dataset_id = tmp_path\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "U6pTk3A9xj1W" + }, + "source": [ + "# Configure for Supervised Fine-Tuning (SFT)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 151, + "referenced_widgets": [ + "388f618924274d21a066f098f4f1e744", + "7c95f85a2b1f47a1bd846d110c47bb3c", + "083f9cda8d754c168beee10d2f8955a2", + "62e1a65582f446a78612eaa804e08a7d", + "487a177d020f4605834878b2fdc7afa3", + "7fd44cf9ca6e4726bfd7ac21846d6a14", + "366a343b62fa47d8985a3bd464d99f9e", + "a0a11e929edd4189b79723d618522c33", + "e87ea87fcff247b5bbcc331ba79a8dc2", + "5e18768f7ad6434ba8b8b8a2e853e204", + "bb33aec33a6447078c31bfd728942994" + ] + }, + "id": "fdRioqytmTtX", + "outputId": "f0acdcec-4b41-4a3f-ffed-c2d2d929158e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2025-05-08 13:40:27,488] [INFO] [root.register:348] [PID:174] Attempting to load plugin: axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin\n", + "[2025-05-08 13:40:27,493] [INFO] [root.register:351] [PID:174] Plugin loaded successfully: axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin\n", + "[2025-05-08 13:40:27,959] [INFO] [axolotl.utils.schemas.config.check_eval_packing:721] [PID:174] [RANK:0] explicitly setting `eval_sample_packing` to match `sample_packing`\u001b[39m\n", + "[2025-05-08 13:40:27,960] [INFO] [axolotl.utils.schemas.config.hint_sample_packing_padding:514] [PID:174] [RANK:0] Setting `pad_to_sequence_len: true` to prevent memory leaks when sample_packing\u001b[39m\n", + "[2025-05-08 13:40:27,961] [INFO] [axolotl.utils.schemas.config.check_bf16:1251] [PID:174] [RANK:0] bf16 support detected, but not enabled for this configuration.\u001b[39m\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "388f618924274d21a066f098f4f1e744", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "config.json: 0%| | 0.00/728 [00:00\"],\n", + " }\n", + " ],\n", + " dataloader_prefetch_factor = 8, # dataloader optimizations\n", + " dataloader_num_workers = 2,\n", + " dataloader_pin_memory = True,\n", + " )\n", + "\n", + "# validates the configuration\n", + "cfg = load_cfg(config)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "715UpvnSoBIS" + }, + "outputs": [], + "source": [ + "from axolotl.utils import patch_optimized_env\n", + "# speedup downloads from HF πŸ€— and set \"PYTORCH_CUDA_ALLOC_CONF\" env to save memory\n", + "patch_optimized_env()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Vc6MC-hwyH-n" + }, + "source": [ + "# Datasets\n", + "\n", + "Axolotl has a robust suite of loaders and transforms to parse most open datasets of any format into the appropriate chat template for your model. Axolotl will mask input tokens from the user's prompt so that the train loss is only calculated against the model's response. For more information, [see our documentation](http://docs.axolotl.ai/docs/dataset-formats/conversation.html) on dataset preparation.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "b82aa8c57f7c422a9a9c90f333ed2a99", + "c0991cf63ee6458b96e9a75e7a88b61a", + "71c8af139cd248b1b51101fd46a93f35", + "1d5117195d4b49eb8f1a73b18419f7ce", + "3c21e4a511b4441192c03b7f1d0976e9", + "ed28e2e0410d4e0b855467e798e53d66", + "d93f134f802b4b69b575bdaf07dbd27c", + "d0e9dce55cec4c1ca619a0ccf209d924", + "4c727d40ef0443449afc31724ee79f0c", + "0dea5caa27384f5689e3cab51f558727", + "a6f48410b9964fefba0c3009a77dc838", + "95caff42f08a4c2aa14c867b8f37f231", + "de7c37ee83e24f0c889e84d07279c2ec", + "9d4897eefb5f48259ffb2d23e332f752", + "253017b0d0534e54ab44e181f6d7c82d", + "27beaf06e41b472abdb544a43c720c5a", + "34cf3df51fbc41cabfdbba153c007f0e", + "ac764024cf1c4e08ba7749afd2cd20ac", + "30a81da86f8043eca301e86a8651201a", + "e8b7a81040904c1e89e58978223b1737", + "1c6f1f10667545aaab958016ba7e2c94", + "e6e969610738449887259063967f82b0", + "a138859f19b74fc0928dc236ab5359db", + "9b42e08b3c9548818488268768a118b1", + "12b56912736849fea2ad8124456fdc5c", + "879c8ab5873847a8833bd74123be90a4", + "20352e5f58d24bb8b1f3940efd14fe4a", + "d955dcaa0e944e719f3a06139dd54a03", + "d3de2662c7964f1ba96e58da382af720", + "97e36007e1304e1583fd81bfb13f0edd", + "c65dc74c7d6f4bab8f7dd28455161dd8", + "ef223e8504b64e3592589880326aaf41", + "598da69727bd4fb8b1caf465ac736d7a", + "5f86cd894de94c3280fadc1e2fd0ee13", + "a20927bf5f2c41f58c1e31ac858ab36c", + "0a46ad75c198463d843fb35e813642cb", + "09007681cf8d42aeb8c1d2f6a74e470a", + "ebc80d1a55fa47f4a5ea2756588569ec", + "1811cda0644e4190a9469d1774435d82", + "35c811d2ae8e43f3b5cecbdd3cfa857f", + "b8e39e4dddc3497fbc29ae45c66da759", + "63b4e563e85c4f03b1b72beda9577bcc", + "b195f160ca20442fadd8b5aed0ee41af", + "ca65e32eb52f48c09a84b33cb18f22cd", + 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"4f1977d7e4824ef1a14b65f0f42bba10", + "c6164e05a1914ae48083db9ad7f4ef7c", + "813621384dc748b0ad06775e22761c0b", + "dc892a596f6942d7973c616c38f0eebb", + "c84cc07789be48aebb322c23d355289e", + "bed8726b8069434687c75452e21f19e5", + "16a188a0b06d45f980dcf3933509fe0a", + "60c1a0d765c14a1d888317e6a507e4ea", + "0077aedc3d174560bce924ee89e9c006", + "00321cce58884f6f9b3855a21fcd9187", + "fa864b41586f4a7aa56aeafd1d84eb75", + "3225603166b54e7aab766b9964a2f660", + "349eee9f56d64f0cba6fc24ff2c50c9b", + "7e5d3774060e4589aa65982da5ea4ef4", + "7c2485c6cdfe463da6fdb35982a1070d", + "ad1236893754446881e153adc9d5c962", + "daee63fd167e4441a32324b51b00ad2b", + "fe41858c6bd04c58840112b67c19a336", + "d262c82138024169b9f3aa034ca756fa", + "62e302ebdad64aada0ffe64ae1c873f3", + "bd1b0dfed6d34d16af33a4a58330f5ec", + "d07c8b97d3314f1c852e44bdd40f61ed", + "ebb69a2c3d0a4299a484698287b3087c", + "e5a82df528bb4e408797a3b6c2758f4a", + "f113ebd8c1c34806bea4dd7ed3035173" + ] + }, + "id": "KQQhgK8FoDfF", + "outputId": "f69441d8-95f9-4885-c306-6c8709090ff6" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b82aa8c57f7c422a9a9c90f333ed2a99", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/9.68k [00:00\u001b[39m\n", + "[2025-05-08 13:41:00,845] [DEBUG] [axolotl.utils.models.load_tokenizer:442] [PID:174] [RANK:0] BOS: None / None\u001b[39m\n", + "[2025-05-08 13:41:00,846] [DEBUG] [axolotl.utils.models.load_tokenizer:443] [PID:174] [RANK:0] PAD: 151643 / <|endoftext|>\u001b[39m\n", + "[2025-05-08 13:41:00,847] [DEBUG] [axolotl.utils.models.load_tokenizer:444] [PID:174] [RANK:0] UNK: None / None\u001b[39m\n", + "[2025-05-08 13:41:00,869] [INFO] [axolotl.utils.data.sft.load_tokenized_prepared_datasets:271] [PID:174] [RANK:0] Unable to find prepared dataset in last_run_prepared/97037817611d38b3a9c681753c3c4c95\u001b[39m\n", + "[2025-05-08 13:41:00,870] [INFO] [axolotl.utils.data.sft.load_tokenized_prepared_datasets:272] [PID:174] [RANK:0] Loading raw datasets...\u001b[39m\n", + "\u001b[33m[2025-05-08 13:41:00,870] [WARNING] [axolotl.utils.data.sft.load_tokenized_prepared_datasets:274] [PID:174] [RANK:0] Processing datasets during training can lead to VRAM instability. Please pre-process your dataset.\u001b[39m\n", + "[2025-05-08 13:41:00,871] [INFO] [axolotl.utils.data.sft.load_tokenized_prepared_datasets:281] [PID:174] [RANK:0] No seed provided, using default seed of 42\u001b[39m\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7cd0b85ebd204b7aba908417811ce4e0", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "train.jsonl: 0%| | 0.00/27.3M [00:00system\\n' }}\n", + " {%- if messages[0].role == 'system' %}\n", + " {{- messages[0].content + '\\n\\n' }}\n", + " {%- endif %}\n", + " {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n", + " {%- for tool in tools %}\n", + " {{- \"\\n\" }}\n", + " {{- tool | tojson }}\n", + " {%- endfor %}\n", + " {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}\n", + "{%- else %}\n", + " {%- if messages[0].role == 'system' %}\n", + " {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n", + " {%- endif %}\n", + "{%- endif %}\n", + "{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n", + "{%- for message in messages[::-1] %}\n", + " {%- set index = (messages|length - 1) - loop.index0 %}\n", + " {%- if ns.multi_step_tool and message.role == \"user\" and not(message.content.startswith('') and message.content.endswith('')) %}\n", + " {%- set ns.multi_step_tool = false %}\n", + " {%- set ns.last_query_index = index %}\n", + " {%- endif %}\n", + "{%- endfor %}\n", + "{%- for message in messages %}\n", + " {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n", + " {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n", + " {%- elif message.role == \"assistant\" %}\n", + " {%- set content = message.content %}\n", + " {%- set reasoning_content = '' %}\n", + " {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n", + " {%- set reasoning_content = message.reasoning_content %}\n", + " {%- else %}\n", + " {%- if '' in message.content %}\n", + " {%- set content = message.content.split('')[-1].lstrip('\\n') %}\n", + " {%- set reasoning_content = message.content.split('')[0].rstrip('\\n').split('')[-1].lstrip('\\n') %}\n", + " {%- endif %}\n", + " {%- endif %}\n", + " {%- if loop.index0 > ns.last_query_index %}\n", + " {%- if loop.last or (not loop.last and reasoning_content) %}\n", + " {{- '<|im_start|>' + message.role + '\\n\\n' + reasoning_content.strip('\\n') + '\\n\\n\\n' + content.lstrip('\\n') }}\n", + " {%- else %}\n", + " {{- '<|im_start|>' + message.role + '\\n' + content }}\n", + " {%- endif %}\n", + " {%- else %}\n", + " {{- '<|im_start|>' + message.role + '\\n' + content }}\n", + " {%- endif %}\n", + " {%- if message.tool_calls %}\n", + " {%- for tool_call in message.tool_calls %}\n", + " {%- if (loop.first and content) or (not loop.first) %}\n", + " {{- '\\n' }}\n", + " {%- endif %}\n", + " {%- if tool_call.function %}\n", + " {%- set tool_call = tool_call.function %}\n", + " {%- endif %}\n", + " {{- '\\n{\"name\": \"' }}\n", + " {{- tool_call.name }}\n", + " {{- '\", \"arguments\": ' }}\n", + " {%- if tool_call.arguments is string %}\n", + " {{- tool_call.arguments }}\n", + " {%- else %}\n", + " {{- tool_call.arguments | tojson }}\n", + " {%- endif %}\n", + " {{- '}\\n' }}\n", + " {%- endfor %}\n", + " {%- endif %}\n", + " {{- '<|im_end|>\\n' }}\n", + " {%- elif message.role == \"tool\" %}\n", + " {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n", + " {{- '<|im_start|>user' }}\n", + " {%- endif %}\n", + " {{- '\\n\\n' }}\n", + " {{- message.content }}\n", + " {{- '\\n' }}\n", + " {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n", + " {{- '<|im_end|>\\n' }}\n", + " {%- endif %}\n", + " {%- endif %}\n", + "{%- endfor %}\n", + "{%- if add_generation_prompt %}\n", + " {{- '<|im_start|>assistant\\n' }}\n", + " {%- if enable_thinking is defined and enable_thinking is false %}\n", + " {{- '\\n\\n\\n\\n' }}\n", + " {%- endif %}\n", + "{%- endif %}\n", + "---\u001b[39m\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "258b7c635c1045329d4669e48c46ccd5", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Tokenizing Prompts (num_proc=2): 0%| | 0/9985 [00:00\n", + " \n", + " \n", + " [25/25 09:25, Epoch 0/1]\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
StepTraining Loss
11.092300
21.554200
31.041400
41.733800
51.430000
61.258500
71.343600
81.101700
91.086500
100.813200
110.689600
120.826700
131.541800
140.948000
151.357000
161.085800
171.516800
181.146800
190.834800
200.968000
211.388800
221.511500
231.338500
241.206600
251.504600

" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2025-05-07 22:12:42,746] [INFO] [axolotl.callbacks.on_step_end:128] [PID:1336] [RANK:0] cuda memory usage while training: 9.768GB (+3.287GB cache, +0.646GB misc)\u001b[39m\n", + "[2025-05-07 22:21:46,859] [INFO] [axolotl.train.save_trained_model:231] [PID:1336] [RANK:0] Training completed! Saving pre-trained model to ./outputs/qwen-sft-pirate-rrr.\u001b[39m\n" + ] + } + ], + "source": [ + "from axolotl.train import train\n", + "\n", + "# just train the first 25 steps for demo.\n", + "# This is sufficient to align the model as we've used packing to maximize the trainable samples per step.\n", + "cfg.max_steps = 25\n", + "model, tokenizer, trainer = train(cfg=cfg, dataset_meta=dataset_meta)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "j1b9ypF78eCb" + }, + "source": [ + "# Inferencing the trained model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "r3_vHhif8YEs", + "outputId": "e5050605-f6c9-421c-98f9-bde56a281eae" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ahoy there, matey! Shiver me timbers, ye be lookin' for the Pythagorean theorem, eh? Well, hold yer horses and listen up, for I'll be tellin' ye all about it in me own special way.\n", + "\n", + "The Pythagorean theorem be a real gem of a mathematical trick that helps ye find the length of a side of a right triangle. Now, a right triangle be a triangle with a right angle, which be that little corner that looks like a square. \n", + "\n", + "The theorem be named after a clever fellow named Pythagoras, who be a mathematician from ancient Greece. He discovered that if ye have a right triangle, the square of the length of the hypotenuse (that be the side opposite the right angle) be equal to the sum of the squares of the other two sides. \n", + "\n", + "In other words, if ye have a triangle with sides of length a, b, and c (\n" + ] + } + ], + "source": [ + "from transformers import TextStreamer\n", + "\n", + "messages = [\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": \"Explain the Pythagorean theorem to me.\",\n", + " },\n", + "]\n", + "\n", + "prompt = tokenizer.apply_chat_template(\n", + " messages,\n", + " add_generation_prompt=True,\n", + " tokenize=False,\n", + " enable_thinking = False,\n", + ")\n", + "\n", + "outputs = model.generate(\n", + " **tokenizer(prompt, return_tensors = \"pt\").to(\"cuda\"),\n", + " max_new_tokens = 192,\n", + " temperature = 1.0, top_p = 0.8, top_k = 32,\n", + " streamer = TextStreamer(tokenizer, skip_prompt = True),\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HoGwT2JRSIjA" + }, + "source": [ + "# Saving your trained model\n", + "\n", + "Axolotl automatically saves checkpoints to the `output_dir` path.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5BmSbiy6NaaS", + "outputId": "f5e1d913-7d55-42d2-8340-f9f1b0bc2b38" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total 506M\n", + "-rw-r--r-- 1 root root 845 May 7 22:21 adapter_config.json\n", + "-rw-r--r-- 1 root root 491M May 7 22:21 adapter_model.safetensors\n", + "-rw-r--r-- 1 root root 707 May 7 22:11 added_tokens.json\n", + "drwxr-xr-x 2 root root 4.0K May 7 22:17 checkpoint-13\n", + "drwxr-xr-x 2 root root 4.0K May 7 22:21 checkpoint-25\n", + "-rw-r--r-- 1 root root 1.2K May 7 22:11 config.json\n", + "-rw-r--r-- 1 root root 1.6M May 7 22:11 merges.txt\n", + "-rw-r--r-- 1 root root 2.6K May 7 22:21 README.md\n", + "-rw-r--r-- 1 root root 613 May 7 22:11 special_tokens_map.json\n", + "-rw-r--r-- 1 root root 9.5K May 7 22:11 tokenizer_config.json\n", + "-rw-r--r-- 1 root root 11M May 7 22:11 tokenizer.json\n", + "-rw-r--r-- 1 root root 2.7M May 7 22:11 vocab.json\n" + ] + } + ], + "source": [ + "# Show the saved checkpoints in the output_dir\n", + "!ls -lh \"./outputs/qwen-sft-pirate-rrr\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_PCIFWxuOZd6" + }, + "source": [ + "Setting `hub_model_id: ` in the original config would have automatically uploaded the model to HuggingFace Hub (e.g. `hub_model_id: username/model_id`)\n", + "\n", + "If you prefer to manually upload the training artifacts, we can still upload the entire final checkpoint to HuggingFace from the CLI." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 955, + "referenced_widgets": [ + "c12ea43372ac4d57bb9605f1a429b397", + "86816687746246b4a6105e8010384e25", + "6f05e9bebf7b40c9835808e77de6c236", + "c7433acd3c4841e6958ae8f7e87b1808", + "19c1e38389fa46c7b7e2152a56e1df34", + "0e067d8db8ed48308a718d5f57683fd1", + "131065f118274a1586ac38e39ed84ef0", + "8640ac440fbc4644b9a3af7ba3ae7183", + "5cea7996f02040b187ece0bb2d6a8d1f", + "2e257c8be2da40b4bb67a9e4ab6811f3", + "56e3768bef5a4b9db4168c5c17f509c2", + "62c028fdef904dedb9cdeca2b3bda725", + "a7cf477e80fc43e0ad82c7997b076dce", + "835bcc28a5564fb9b3d651bc8e32dc46", + "9f1c9a0695384bdaa6f8b847ef89bee8", + "b1bea589efa14258a9982071b87938bf", + "590eef89881545aa8bbef9a8bbe7fb00", + "4b1f04ff63d14a118fdd15814dff50e4", + "39789237703c4a418134243055c9cbf5", + "a3a945817f684328b34651fe052393ec" + ] + }, + "id": "2yw8pLvlSMl8", + "outputId": "6e489ab2-4abe-4e28-84ca-959f912433a4" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c12ea43372ac4d57bb9605f1a429b397", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(HTML(value='

\n", + " sys.exit(main())\n", + " ^^^^^^\n", + " File \"/usr/local/lib/python3.11/dist-packages/huggingface_hub/commands/huggingface_cli.py\", line 57, in main\n", + " service.run()\n", + " File \"/usr/local/lib/python3.11/dist-packages/huggingface_hub/commands/upload.py\", line 207, in run\n", + " print(self._upload())\n", + " ^^^^^^^^^^^^^^\n", + " File \"/usr/local/lib/python3.11/dist-packages/huggingface_hub/commands/upload.py\", line 302, in _upload\n", + " return self.api.upload_folder(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_validators.py\", line 114, in _inner_fn\n", + " return fn(*args, **kwargs)\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"/usr/local/lib/python3.11/dist-packages/huggingface_hub/hf_api.py\", line 1633, in _inner\n", + " return fn(self, *args, **kwargs)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/usr/local/lib/python3.11/dist-packages/huggingface_hub/hf_api.py\", line 4942, in upload_folder\n", + " commit_info = self.create_commit(\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_validators.py\", line 114, in _inner_fn\n", + " return fn(*args, **kwargs)\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"/usr/local/lib/python3.11/dist-packages/huggingface_hub/hf_api.py\", line 1633, in _inner\n", + " return fn(self, *args, **kwargs)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/usr/local/lib/python3.11/dist-packages/huggingface_hub/hf_api.py\", line 4202, in create_commit\n", + " self.preupload_lfs_files(\n", + " File \"/usr/local/lib/python3.11/dist-packages/huggingface_hub/hf_api.py\", line 4483, in preupload_lfs_files\n", + " _upload_xet_files(**upload_kwargs, create_pr=create_pr) # type: ignore [arg-type]\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_validators.py\", line 114, in _inner_fn\n", + " return fn(*args, **kwargs)\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"/usr/local/lib/python3.11/dist-packages/huggingface_hub/_commit_api.py\", line 592, in _upload_xet_files\n", + " with progress_cm as progress:\n", + " File \"/usr/local/lib/python3.11/dist-packages/tqdm/std.py\", line 1138, in __exit__\n", + " def __exit__(self, exc_type, exc_value, traceback):\n", + "\n", + "KeyboardInterrupt\n", + "^C\n" + ] + } + ], + "source": [ + "from huggingface_hub import notebook_login\n", + "# remove the partial epoch checkpoints\n", + "!rm -rf \"./outputs/qwen-sft-pirate-rrr/checkpoint-*\"\n", + "\n", + "# HF Notebook login widget\n", + "notebook_login()\n", + "\n", + "# upload the LoRA adapter for your model to HF, remember to update the username/model-name below\n", + "!huggingface-cli upload --repo-type=model winglian/pirate-qwen-14B \"./outputs/qwen-sft-pirate-rrr\"" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { 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" 3.96G/3.96G [00:12<00:00, 656MB/s]" + } + } + } + } + }, + "nbformat": 4, + "nbformat_minor": 0 } diff --git a/requirements.txt b/requirements.txt index 67598c91a..1cbb33721 100644 --- a/requirements.txt +++ b/requirements.txt @@ -76,3 +76,4 @@ mistral-common==1.8.3 # TUI dependencies textual==1.0.0 rich==14.1.0 +tree_sitter_ruby==0.23.1 diff --git a/src/axolotl/prompt_strategies/messages/chat.py b/src/axolotl/prompt_strategies/messages/chat.py index eaed2396a..f80b913f4 100644 --- a/src/axolotl/prompt_strategies/messages/chat.py +++ b/src/axolotl/prompt_strategies/messages/chat.py @@ -72,9 +72,10 @@ def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None): builder_kwargs["message_field_training"] = message_field_training chat_template = ds_cfg.get("chat_template", cfg.get("chat_template", "chatml")) - format_message = ( - lambda x: x # noqa E731 # pylint: disable=unnecessary-lambda-assignment - ) + + def format_message(x): + return x + if chat_template == "chatml": from axolotl.core.chat.format.chatml import format_message # noqa F811 if chat_template.startswith("llama3"): diff --git a/src/axolotl/tui/app.py b/src/axolotl/tui/app.py index 7a6cdb1fd..42649491d 100644 --- a/src/axolotl/tui/app.py +++ b/src/axolotl/tui/app.py @@ -3,9 +3,9 @@ from textual import on from textual.app import App, ComposeResult from textual.binding import Binding -from textual.containers import Container, Horizontal, ScrollableContainer +from textual.containers import Container from textual.screen import Screen -from textual.widgets import Button, Footer, Header, Label, Static +from textual.widgets import Button, Footer, Header, Static from axolotl.tui.screens.config import ConfigScreen from axolotl.tui.screens.datasets import DatasetScreen @@ -117,7 +117,6 @@ class AxolotlTUI(App): text-style: bold; padding: 1; color: $primary; - content-align: center middle; } .subtitle { @@ -129,6 +128,7 @@ class AxolotlTUI(App): .welcome-container { align: center middle; height: 100%; + width: 100%; } .menu-container { @@ -144,7 +144,7 @@ class AxolotlTUI(App): margin: 1; } - Screen { + WelcomeScreen { align: center middle; } """ diff --git a/src/axolotl/tui/dialogs/training.py b/src/axolotl/tui/dialogs/training.py index 837f011d7..b2abde122 100644 --- a/src/axolotl/tui/dialogs/training.py +++ b/src/axolotl/tui/dialogs/training.py @@ -4,7 +4,7 @@ from pathlib import Path from textual import on from textual.app import ComposeResult -from textual.containers import Container, Horizontal, Vertical +from textual.containers import Container from textual.screen import ModalScreen from textual.widgets import Button, Input, Label, Select, Static diff --git a/src/axolotl/tui/screens/config.py b/src/axolotl/tui/screens/config.py index 3eabd4a8e..595d0f720 100644 --- a/src/axolotl/tui/screens/config.py +++ b/src/axolotl/tui/screens/config.py @@ -8,26 +8,19 @@ import yaml from textual import on, work from textual.app import ComposeResult from textual.binding import Binding -from textual.containers import Container, Horizontal, ScrollableContainer, Vertical -from textual.message import Message +from textual.containers import Container from textual.reactive import reactive from textual.widgets import ( Button, - DataTable, DirectoryTree, Footer, Header, - Input, Label, - LoadingIndicator, Log, - Select, Static, - Switch, TextArea, ) -from axolotl.cli.config import load_cfg from axolotl.tui.screens.base import BaseScreen @@ -154,7 +147,7 @@ class ConfigScreen(BaseScreen): classes="config-actions", ), Container( - Log(id="validation-log", wrap=True, highlight=True), + Log(id="validation-log"), classes="validation-log", ), classes="config-editor", diff --git a/src/axolotl/tui/screens/datasets.py b/src/axolotl/tui/screens/datasets.py index 22670b096..821fced5e 100644 --- a/src/axolotl/tui/screens/datasets.py +++ b/src/axolotl/tui/screens/datasets.py @@ -2,27 +2,21 @@ import json from pathlib import Path -from typing import Dict, List, Optional +from typing import Dict, Optional from textual import on, work from textual.app import ComposeResult from textual.binding import Binding -from textual.containers import Container, Horizontal, ScrollableContainer, Vertical +from textual.containers import Container from textual.widgets import ( Button, DataTable, Footer, Header, - Input, Label, - LoadingIndicator, Log, - Pretty, ProgressBar, - Select, Static, - TabbedContent, - TabPane, TextArea, ) @@ -76,7 +70,7 @@ class DatasetScreen(BaseScreen): .preview-container { height: 100%; - border: solid $info; + border: solid $primary; padding: 1; } @@ -157,84 +151,25 @@ class DatasetScreen(BaseScreen): classes="dataset-list", ), Container( - TabbedContent( - TabPane( - "Preview", - Container( - TextArea( - "", - language="json", - theme="monokai", - id="dataset-preview", - read_only=True, - ), - classes="preview-container", - ), - ), - TabPane( - "Statistics", - Container( - Container( - Static("Dataset Name:", classes="stat-label"), - Static("-", id="stat-name", classes="stat-value"), - classes="stat-row", - ), - Container( - Static("Type:", classes="stat-label"), - Static("-", id="stat-type", classes="stat-value"), - classes="stat-row", - ), - Container( - Static("Size:", classes="stat-label"), - Static("-", id="stat-size", classes="stat-value"), - classes="stat-row", - ), - Container( - Static("Samples:", classes="stat-label"), - Static( - "-", id="stat-samples", classes="stat-value" - ), - classes="stat-row", - ), - Container( - Static("Features:", classes="stat-label"), - Static( - "-", id="stat-features", classes="stat-value" - ), - classes="stat-row", - ), - Container( - Static("Format:", classes="stat-label"), - Static("-", id="stat-format", classes="stat-value"), - classes="stat-row", - ), - Container( - Static("Preprocessed:", classes="stat-label"), - Static( - "-", - id="stat-preprocessed", - classes="stat-value", - ), - classes="stat-row", - ), - classes="stats-container", - ), - ), - TabPane( - "Processing", - Container( - Log(id="processing-log", wrap=True, highlight=True), - Container( - Label("Preprocessing Progress:"), - ProgressBar( - total=100, - id="preprocessing-progress", - ), - classes="progress-container", - ), - ), - ), + TextArea("", id="dataset-preview", read_only=True), + Container( + Static("Dataset Name:", classes="stat-label"), + Static("-", id="stat-name", classes="stat-value"), + Static("Type:", classes="stat-label"), + Static("-", id="stat-type", classes="stat-value"), + Static("Size:", classes="stat-label"), + Static("-", id="stat-size", classes="stat-value"), + Static("Samples:", classes="stat-label"), + Static("-", id="stat-samples", classes="stat-value"), + Static("Features:", classes="stat-label"), + Static("-", id="stat-features", classes="stat-value"), + Static("Format:", classes="stat-label"), + Static("-", id="stat-format", classes="stat-value"), + Static("Preprocessed:", classes="stat-label"), + Static("-", id="stat-preprocessed", classes="stat-value"), ), + Log(id="processing-log"), + ProgressBar(total=100, id="preprocessing-progress"), classes="dataset-details", ), classes="dataset-container", @@ -325,10 +260,10 @@ class DatasetScreen(BaseScreen): @on(DataTable.RowSelected) def handle_dataset_selected(self, event: DataTable.RowSelected) -> None: """Handle dataset selection from table.""" - if event.row_index >= 0: + if event.cursor_row >= 0: dataset_names = list(self.datasets.keys()) - if event.row_index < len(dataset_names): - self.selected_dataset = dataset_names[event.row_index] + if event.cursor_row < len(dataset_names): + self.selected_dataset = dataset_names[event.cursor_row] self.load_dataset_preview() self.update_dataset_stats() diff --git a/src/axolotl/tui/screens/inference.py b/src/axolotl/tui/screens/inference.py index df68c8eec..2a5c19549 100644 --- a/src/axolotl/tui/screens/inference.py +++ b/src/axolotl/tui/screens/inference.py @@ -3,16 +3,15 @@ from pathlib import Path from typing import Dict, List, Optional -from textual import on, work +from textual import events, on, work from textual.app import ComposeResult from textual.binding import Binding -from textual.containers import Container, Horizontal, ScrollableContainer, Vertical +from textual.containers import Container from textual.widgets import ( Button, Input, Label, Log, - Pretty, Select, Static, TextArea, @@ -53,7 +52,7 @@ class InferenceScreen(BaseScreen): .chat-history { height: 70%; - border: solid $info; + border: solid $primary; padding: 1; margin: 0 0 1 0; } @@ -121,7 +120,7 @@ class InferenceScreen(BaseScreen): Container( Label("Model Selection"), Select( - [("none", "No model loaded")], + [("No model loaded", "none")], id="model-select", value="none", ), @@ -147,12 +146,11 @@ class InferenceScreen(BaseScreen): ), Container( Container( - Log(id="chat-history", wrap=True, highlight=True), + Log(id="chat-history"), classes="chat-history", ), Container( TextArea( - placeholder="Type your message here...", id="message-input", ), classes="input-area", @@ -182,27 +180,44 @@ class InferenceScreen(BaseScreen): @work(thread=True) async def load_available_models(self) -> None: """Load list of available models.""" - models = [("none", "No model loaded")] + models = [("No model loaded", "none")] + + chat = self.query_one("#chat-history", Log) + chat.write_line("πŸ” Scanning for available models...") # Check for trained models outputs_dir = Path("./outputs") + chat.write_line(f"Checking outputs directory: {outputs_dir.absolute()}") if outputs_dir.exists(): + found_models = 0 for model_dir in outputs_dir.glob("*"): - if model_dir.is_dir() and (model_dir / "pytorch_model.bin").exists(): - models.append((str(model_dir), model_dir.name)) - - # Check for HuggingFace models in cache - hf_cache = Path.home() / ".cache" / "huggingface" / "transformers" - if hf_cache.exists(): - for model_dir in hf_cache.glob("models--*"): if model_dir.is_dir(): - model_name = model_dir.name.replace("models--", "").replace( - "--", "/" + # Look for various model file types + model_files = ( + list(model_dir.glob("pytorch_model.bin")) + + list(model_dir.glob("model.safetensors")) + + list(model_dir.glob("*.bin")) + + list(model_dir.glob("*.safetensors")) ) - models.append((str(model_dir), f"HF: {model_name}")) + if model_files: + models.append((model_dir.name, str(model_dir))) + found_models += 1 + chat.write_line(f"Found {found_models} trained models in outputs/") + else: + chat.write_line("outputs/ directory not found") + + # Add some example/demo models for testing + models.extend( + [ + ("Demo: GPT-2 Small", "gpt2"), + ("Demo: TinyLlama", "TinyLlama/TinyLlama-1.1B-Chat-v1.0"), + ("Demo: Phi-2", "microsoft/phi-2"), + ] + ) select = self.query_one("#model-select", Select) select.set_options(models) + chat.write_line(f"βœ… Loaded {len(models)} models in dropdown") @on(Button.Pressed, "#load-model") @work(thread=True) @@ -258,28 +273,63 @@ class InferenceScreen(BaseScreen): self.chat_history.append({"role": "user", "content": message}) # Generate response (placeholder) - await self.generate_response(message) + self.generate_response(message) + + @on(TextArea.Changed, "#message-input") + def on_message_input_changed(self, event: TextArea.Changed) -> None: + """Handle changes to the message input.""" + # This could be used for features like typing indicators + pass + + def on_key(self, event: events.Key) -> None: + """Handle key events globally.""" + # Check if we're focused on the message input and Ctrl+Enter is pressed + focused = self.focused + if focused and focused.id == "message-input" and event.key == "ctrl+enter": + event.prevent_default() + self.handle_send_message() @work(thread=True) async def generate_response(self, message: str) -> None: - """Generate model response (placeholder implementation).""" + """Generate model response.""" chat = self.query_one("#chat-history", Log) chat.write_line("πŸ€– Assistant: Thinking...") try: # Get inference parameters - temperature = float(self.query_one("#temperature", Input).value) - max_tokens = int(self.query_one("#max-tokens", Input).value) - top_p = float(self.query_one("#top-p", Input).value) + float(self.query_one("#temperature", Input).value) + int(self.query_one("#max-tokens", Input).value) + float(self.query_one("#top-p", Input).value) - # Placeholder response (in real implementation, would call the model) + if not self.loaded_model or self.loaded_model == "none": + response = "I don't have a model loaded yet. Please load a model first using the 'Load Model' button." + elif self.loaded_model.startswith("gpt2"): + # Simple response for GPT-2 + responses = [ + f"Thanks for your message: '{message}'. I'm a GPT-2 model running in demo mode.", + "I understand you're testing the interface. GPT-2 models are great for experimentation!", + "This is a simulated GPT-2 response. In a real setup, I'd generate text based on your input.", + f"GPT-2 here! You said: '{message}'. I'd normally continue this conversation creatively.", + ] + import random + + response = random.choice(responses) + elif "llama" in self.loaded_model.lower(): + # Response for Llama models + response = f"πŸ¦™ LLaMA model here! You asked: '{message}'. I'm designed for helpful, harmless, and honest conversations. How can I assist you today?" + elif "phi" in self.loaded_model.lower(): + # Response for Phi models + response = f"Phi model responding! Your message: '{message}'. I'm optimized for reasoning and code tasks. What would you like to explore?" + else: + # Generic response for other models + response = f"Model '{self.loaded_model}' responding to: '{message}'. I'm ready to help with your questions!" + + # Simulate inference time import time - time.sleep(1) # Simulate inference time + time.sleep(0.5) - response = f"This is a placeholder response to: '{message}'. In a real implementation, this would be generated by the loaded model using the parameters: temperature={temperature}, max_tokens={max_tokens}, top_p={top_p}." - - # Update chat with response + # Clear the "thinking" message and show response chat.write_line(f"πŸ€– Assistant: {response}") # Add to history diff --git a/src/axolotl/tui/screens/models.py b/src/axolotl/tui/screens/models.py index 9e0acc723..b2c673786 100644 --- a/src/axolotl/tui/screens/models.py +++ b/src/axolotl/tui/screens/models.py @@ -1,22 +1,20 @@ """Model management screen for Axolotl TUI.""" from pathlib import Path -from typing import Dict, List, Optional +from typing import Dict, Optional from textual import on, work from textual.app import ComposeResult from textual.binding import Binding -from textual.containers import Container, Horizontal, ScrollableContainer +from textual.containers import Container, ScrollableContainer from textual.widgets import ( Button, DataTable, Footer, Header, - Input, Label, Log, ProgressBar, - Select, Static, TabbedContent, TabPane, @@ -96,56 +94,38 @@ class ModelScreen(BaseScreen): def compose(self) -> ComposeResult: """Compose the model screen layout.""" yield Header() - yield Container( - Static("🦾 Model Management", classes="screen-title"), - Static( + with Container(id="content"): + yield Static("🦾 Model Management", classes="screen-title") + yield Static( "Manage trained models, merge LoRA adapters, and quantize models", classes="screen-subtitle", - ), - Container( - Container( - Label("Available Models"), - DataTable(id="model-table"), - Container( - Button("Merge LoRA", id="merge-lora", variant="primary"), - Button("Quantize", id="quantize", variant="success"), - Button("Evaluate", id="evaluate", variant="warning"), - Button("Refresh", id="refresh", variant="default"), - classes="model-actions", - ), - classes="model-list", - ), - Container( - TabbedContent( - TabPane( - "Operations", - Container( - Log(id="operations-log", wrap=True, highlight=True), - Container( - Label("Operation Progress:"), - ProgressBar( + ) + with Container(classes="model-container"): + with Container(classes="model-list"): + yield Label("Available Models") + yield DataTable(id="model-table") + with Container(classes="model-actions"): + yield Button("Merge LoRA", id="merge-lora", variant="primary") + yield Button("Quantize", id="quantize", variant="success") + yield Button("Evaluate", id="evaluate", variant="warning") + yield Button("Refresh", id="refresh", variant="default") + with Container(classes="model-operations"): + with TabbedContent(): + with TabPane("Operations"): + with Container(): + yield Log(id="operations-log") + with Container(): + yield Label("Operation Progress:") + yield ProgressBar( total=100, id="operation-progress", - ), - ), - ), - ), - TabPane( - "Model Info", - ScrollableContainer( - Static( + ) + with TabPane("Model Info"): + with ScrollableContainer(): + yield Static( "Model information will appear here", id="model-info", - ), - ), - ), - ), - classes="model-operations", - ), - classes="model-container", - ), - id="content", - ) + ) yield Footer() def on_mount(self) -> None: @@ -210,10 +190,10 @@ class ModelScreen(BaseScreen): @on(DataTable.RowSelected) def handle_model_selected(self, event: DataTable.RowSelected) -> None: """Handle model selection from table.""" - if event.row_index >= 0: + if event.cursor_row >= 0: model_names = list(self.models.keys()) - if event.row_index < len(model_names): - self.selected_model = model_names[event.row_index] + if event.cursor_row < len(model_names): + self.selected_model = model_names[event.cursor_row] self.update_model_info() def update_model_info(self) -> None: diff --git a/src/axolotl/tui/screens/monitor.py b/src/axolotl/tui/screens/monitor.py index a0eca8829..3b06758b6 100644 --- a/src/axolotl/tui/screens/monitor.py +++ b/src/axolotl/tui/screens/monitor.py @@ -4,7 +4,7 @@ import psutil from textual import on, work from textual.app import ComposeResult from textual.binding import Binding -from textual.containers import Container, Horizontal, Vertical +from textual.containers import Container from textual.widgets import ( Button, DataTable, @@ -15,8 +15,6 @@ from textual.widgets import ( ProgressBar, Sparkline, Static, - TabbedContent, - TabPane, ) from axolotl.tui.screens.base import BaseScreen @@ -59,7 +57,6 @@ class MonitorScreen(BaseScreen): text-style: bold; text-align: center; padding: 1; - font-size: 2; } .charts-container { @@ -70,7 +67,7 @@ class MonitorScreen(BaseScreen): .chart-panel { width: 50%; - border: solid $info; + border: solid $primary; padding: 1; margin: 0 1; } @@ -179,36 +176,9 @@ class MonitorScreen(BaseScreen): classes="charts-container", ), Container( - TabbedContent( - TabPane( - "Processes", - Container( - DataTable(id="process-table"), - Container( - Button( - "Kill Process", - id="kill-process", - variant="error", - ), - Button("Refresh", id="refresh", variant="default"), - Button( - "Auto Refresh", - id="auto-refresh", - variant="primary", - ), - classes="process-controls", - ), - ), - ), - TabPane( - "GPU Info", - Log(id="gpu-info", wrap=True, highlight=True), - ), - TabPane( - "System Logs", - Log(id="system-logs", wrap=True, highlight=True), - ), - ), + DataTable(id="process-table"), + Log(id="gpu-info"), + Log(id="system-logs"), classes="processes-container", ), classes="monitor-container", @@ -360,7 +330,7 @@ class MonitorScreen(BaseScreen): """Update system information.""" try: # System info - boot_time = psutil.boot_time() + psutil.boot_time() cpu_count = psutil.cpu_count() memory = psutil.virtual_memory() diff --git a/src/axolotl/tui/screens/training.py b/src/axolotl/tui/screens/training.py index 8658f653a..eb7297de6 100644 --- a/src/axolotl/tui/screens/training.py +++ b/src/axolotl/tui/screens/training.py @@ -1,7 +1,5 @@ """Training management screen for Axolotl TUI.""" -import asyncio -import os import subprocess import threading from dataclasses import dataclass @@ -12,24 +10,16 @@ from typing import Dict, List, Optional from textual import on, work from textual.app import ComposeResult from textual.binding import Binding -from textual.containers import Container, Horizontal, ScrollableContainer, Vertical -from textual.reactive import reactive +from textual.containers import Container from textual.widgets import ( Button, DataTable, Footer, Header, - Input, Label, - LoadingIndicator, Log, - ProgressBar, - Select, Sparkline, Static, - Switch, - TabbedContent, - TabPane, ) from axolotl.tui.screens.base import BaseScreen @@ -101,7 +91,7 @@ class TrainingScreen(BaseScreen): .metrics-panel { layout: horizontal; height: 10; - border: solid $info; + border: solid $primary; padding: 1; margin: 1; } @@ -227,20 +217,7 @@ class TrainingScreen(BaseScreen): classes="sparkline-container", ), Container( - TabbedContent( - TabPane( - "Training Logs", - Log(id="training-logs", wrap=True, highlight=True), - ), - TabPane( - "System Logs", - Log(id="system-logs", wrap=True, highlight=True), - ), - TabPane( - "Validation", - Log(id="validation-logs", wrap=True, highlight=True), - ), - ), + Log(id="training-logs"), classes="log-viewer", ), classes="job-details-container", @@ -338,10 +315,10 @@ class TrainingScreen(BaseScreen): @on(DataTable.RowSelected) def handle_row_selected(self, event: DataTable.RowSelected) -> None: """Handle job selection from table.""" - if event.row_index >= 0: + if event.cursor_row >= 0: job_ids = list(self.jobs.keys()) - if event.row_index < len(job_ids): - self.selected_job_id = job_ids[event.row_index] + if event.cursor_row < len(job_ids): + self.selected_job_id = job_ids[event.cursor_row] self.update_selected_job_metrics() self.load_job_logs()