llm-course
/
Fine_tune_LLMs_with_Axolotl.ipynb
1555 строк · 76.6 Кб
1{
2"cells": [
3{
4"cell_type": "markdown",
5"metadata": {
6"id": "view-in-github",
7"colab_type": "text"
8},
9"source": [
10"<a href=\"https://colab.research.google.com/github/mlabonne/llm-course/blob/main/Fine_tune_LLMs_with_Axolotl.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
11]
12},
13{
14"cell_type": "markdown",
15"source": [
16"# Fine-tune LLMs with Axolotl\n",
17"\n",
18"> 🗣️ [Large Language Model Course](https://github.com/mlabonne/llm-course)\n",
19"\n",
20"❤️ Created by [@maximelabonne](https://twitter.com/maximelabonne), based on [Giorgio](https://github.com/g-i-o-r-g-i-o)'s notebook and Axolotl's [example](https://github.com/OpenAccess-AI-Collective/axolotl/blob/main/examples/colab-notebooks/colab-axolotl-example.ipynb)."
21],
22"metadata": {
23"id": "mL-BPZyZ0gtV"
24}
25},
26{
27"cell_type": "code",
28"source": [
29"!git clone -q https://github.com/OpenAccess-AI-Collective/axolotl\n",
30"%cd axolotl\n",
31"!pip install -qqq packaging huggingface_hub --progress-bar off\n",
32"!pip install -qqq -e '.[flash-attn,deepspeed]' --progress-bar off"
33],
34"metadata": {
35"id": "BI6B0Bfe0hxr"
36},
37"execution_count": null,
38"outputs": []
39},
40{
41"cell_type": "code",
42"source": [
43"import yaml\n",
44"\n",
45"new_model = \"mlabonne/TinyAlpaca\"\n",
46"yaml_string = \"\"\"\n",
47"base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T\n",
48"model_type: LlamaForCausalLM\n",
49"tokenizer_type: LlamaTokenizer\n",
50"is_llama_derived_model: true\n",
51"\n",
52"load_in_8bit: false\n",
53"load_in_4bit: true\n",
54"strict: false\n",
55"\n",
56"datasets:\n",
57" - path: mhenrichsen/alpaca_2k_test\n",
58" type: alpaca\n",
59"dataset_prepared_path:\n",
60"val_set_size: 0.05\n",
61"output_dir: ./qlora-out\n",
62"\n",
63"adapter: qlora\n",
64"lora_model_dir:\n",
65"\n",
66"sequence_len: 1096\n",
67"sample_packing: true\n",
68"pad_to_sequence_len: true\n",
69"\n",
70"lora_r: 32\n",
71"lora_alpha: 16\n",
72"lora_dropout: 0.05\n",
73"lora_target_modules:\n",
74"lora_target_linear: true\n",
75"lora_fan_in_fan_out:\n",
76"\n",
77"wandb_project:\n",
78"wandb_entity:\n",
79"wandb_watch:\n",
80"wandb_name:\n",
81"wandb_log_model:\n",
82"\n",
83"mlflow_experiment_name: colab-example\n",
84"\n",
85"gradient_accumulation_steps: 1\n",
86"micro_batch_size: 1\n",
87"num_epochs: 4\n",
88"max_steps: 20\n",
89"optimizer: paged_adamw_32bit\n",
90"lr_scheduler: cosine\n",
91"learning_rate: 0.0002\n",
92"\n",
93"train_on_inputs: false\n",
94"group_by_length: false\n",
95"bf16: false\n",
96"fp16: true\n",
97"tf32: false\n",
98"\n",
99"gradient_checkpointing: true\n",
100"early_stopping_patience:\n",
101"resume_from_checkpoint:\n",
102"local_rank:\n",
103"logging_steps: 1\n",
104"xformers_attention:\n",
105"flash_attention: false\n",
106"\n",
107"warmup_steps: 10\n",
108"evals_per_epoch:\n",
109"saves_per_epoch:\n",
110"debug:\n",
111"deepspeed:\n",
112"weight_decay: 0.0\n",
113"fsdp:\n",
114"fsdp_config:\n",
115"special_tokens:\n",
116"\n",
117"\"\"\"\n",
118"\n",
119"# Convert the YAML string to a Python dictionary\n",
120"yaml_dict = yaml.safe_load(yaml_string)\n",
121"\n",
122"# Specify your file path\n",
123"yaml_file = 'config.yaml'\n",
124"\n",
125"# Write the YAML file\n",
126"with open(yaml_file, 'w') as file:\n",
127" yaml.dump(yaml_dict, file)"
128],
129"metadata": {
130"id": "70zJf1hi0huQ"
131},
132"execution_count": 5,
133"outputs": []
134},
135{
136"cell_type": "code",
137"execution_count": 6,
138"metadata": {
139"colab": {
140"base_uri": "https://localhost:8080/"
141},
142"id": "ffcc0IB9Sr-z",
143"outputId": "f64291b0-0867-440b-b7b9-683fa66e3a08"
144},
145"outputs": [
146{
147"output_type": "stream",
148"name": "stdout",
149"text": [
150"The following values were not passed to `accelerate launch` and had defaults used instead:\n",
151"\t`--num_processes` was set to a value of `1`\n",
152"\t`--num_machines` was set to a value of `1`\n",
153"\t`--mixed_precision` was set to a value of `'no'`\n",
154"\t`--dynamo_backend` was set to a value of `'no'`\n",
155"To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`.\n",
156"2024-01-27 22:05:58.426793: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
157"2024-01-27 22:05:58.426851: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
158"2024-01-27 22:05:58.428152: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
159"2024-01-27 22:05:59.454203: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
160"[2024-01-27 22:06:00,824] [INFO] [datasets.<module>:58] [PID:2797] PyTorch version 2.1.2 available.\n",
161"[2024-01-27 22:06:00,825] [INFO] [datasets.<module>:95] [PID:2797] TensorFlow version 2.15.0 available.\n",
162"[2024-01-27 22:06:00,827] [INFO] [datasets.<module>:108] [PID:2797] JAX version 0.4.23 available.\n",
163"[2024-01-27 22:06:01,844] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n",
164"config.json: 100% 560/560 [00:00<00:00, 3.84MB/s]\n",
165"[2024-01-27 22:06:03,638] [INFO] [axolotl.normalize_config:170] [PID:2797] [RANK:0] GPU memory usage baseline: 0.000GB (+0.255GB misc)\u001b[39m\n",
166" dP dP dP \n",
167" 88 88 88 \n",
168" .d8888b. dP. .dP .d8888b. 88 .d8888b. d8888P 88 \n",
169" 88' `88 `8bd8' 88' `88 88 88' `88 88 88 \n",
170" 88. .88 .d88b. 88. .88 88 88. .88 88 88 \n",
171" `88888P8 dP' `dP `88888P' dP `88888P' dP dP \n",
172" \n",
173" \n",
174"\n",
175"\u001b[33m[2024-01-27 22:06:03,642] [WARNING] [axolotl.scripts.check_user_token:382] [PID:2797] [RANK:0] Error verifying HuggingFace token. Remember to log in using `huggingface-cli login` and get your access token from https://huggingface.co/settings/tokens if you want to use gated models or datasets.\u001b[39m\n",
176"tokenizer_config.json: 100% 776/776 [00:00<00:00, 4.70MB/s]\n",
177"tokenizer.model: 100% 500k/500k [00:00<00:00, 1.77MB/s]\n",
178"special_tokens_map.json: 100% 414/414 [00:00<00:00, 1.73MB/s]\n",
179"tokenizer.json: 100% 1.84M/1.84M [00:00<00:00, 4.52MB/s]\n",
180"[2024-01-27 22:06:05,850] [DEBUG] [axolotl.load_tokenizer:216] [PID:2797] [RANK:0] EOS: 2 / </s>\u001b[39m\n",
181"[2024-01-27 22:06:05,850] [DEBUG] [axolotl.load_tokenizer:217] [PID:2797] [RANK:0] BOS: 1 / <s>\u001b[39m\n",
182"[2024-01-27 22:06:05,850] [DEBUG] [axolotl.load_tokenizer:218] [PID:2797] [RANK:0] PAD: 2 / </s>\u001b[39m\n",
183"[2024-01-27 22:06:05,850] [DEBUG] [axolotl.load_tokenizer:219] [PID:2797] [RANK:0] UNK: 0 / <unk>\u001b[39m\n",
184"[2024-01-27 22:06:05,850] [INFO] [axolotl.load_tokenizer:230] [PID:2797] [RANK:0] No Chat template selected. Consider adding a chat template for easier inference.\u001b[39m\n",
185"[2024-01-27 22:06:05,850] [INFO] [axolotl.load_tokenized_prepared_datasets:182] [PID:2797] [RANK:0] Unable to find prepared dataset in last_run_prepared/c0112363192ff19da1e486577d4bf28b\u001b[39m\n",
186"[2024-01-27 22:06:05,850] [INFO] [axolotl.load_tokenized_prepared_datasets:183] [PID:2797] [RANK:0] Loading raw datasets...\u001b[39m\n",
187"\u001b[33m[2024-01-27 22:06:05,850] [WARNING] [axolotl.load_tokenized_prepared_datasets:185] [PID:2797] [RANK:0] Processing datasets during training can lead to VRAM instability. Please pre-process your dataset.\u001b[39m\n",
188"[2024-01-27 22:06:05,850] [INFO] [axolotl.load_tokenized_prepared_datasets:192] [PID:2797] [RANK:0] No seed provided, using default seed of 42\u001b[39m\n",
189"Downloading readme: 100% 28.0/28.0 [00:00<00:00, 219kB/s]\n",
190"Downloading data: 100% 1.76M/1.76M [00:00<00:00, 2.45MB/s]\n",
191"Generating train split: 2000 examples [00:00, 37778.35 examples/s]\n",
192"Tokenizing Prompts (num_proc=2): 100% 2000/2000 [00:05<00:00, 352.09 examples/s]\n",
193"[2024-01-27 22:06:17,586] [INFO] [axolotl.load_tokenized_prepared_datasets:395] [PID:2797] [RANK:0] merging datasets\u001b[39m\n",
194"Dropping Long Sequences (num_proc=2): 100% 2000/2000 [00:00<00:00, 2927.43 examples/s]\n",
195"Add position_id column (Sample Packing) (num_proc=2): 100% 2000/2000 [00:00<00:00, 3089.00 examples/s]\n",
196"[2024-01-27 22:06:19,041] [INFO] [axolotl.load_tokenized_prepared_datasets:405] [PID:2797] [RANK:0] Saving merged prepared dataset to disk... last_run_prepared/c0112363192ff19da1e486577d4bf28b\u001b[39m\n",
197"Saving the dataset (1/1 shards): 100% 2000/2000 [00:00<00:00, 142755.66 examples/s]\n",
198"[2024-01-27 22:06:19,067] [DEBUG] [axolotl.log:61] [PID:2797] [RANK:0] total_num_tokens: 22777\u001b[39m\n",
199"[2024-01-27 22:06:19,069] [DEBUG] [axolotl.log:61] [PID:2797] [RANK:0] `total_supervised_tokens: 16719`\u001b[39m\n",
200"[2024-01-27 22:06:24,965] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 1.0 total_num_tokens per device: 22777\u001b[39m\n",
201"[2024-01-27 22:06:24,965] [DEBUG] [axolotl.log:61] [PID:2797] [RANK:0] data_loader_len: 19\u001b[39m\n",
202"[2024-01-27 22:06:24,966] [INFO] [axolotl.log:61] [PID:2797] [RANK:0] sample_packing_eff_est across ranks: [0.8659139294403893]\u001b[39m\n",
203"[2024-01-27 22:06:24,966] [DEBUG] [axolotl.log:61] [PID:2797] [RANK:0] sample_packing_eff_est: None\u001b[39m\n",
204"[2024-01-27 22:06:24,966] [DEBUG] [axolotl.log:61] [PID:2797] [RANK:0] total_num_steps: 76\u001b[39m\n",
205"[2024-01-27 22:06:24,970] [DEBUG] [axolotl.log:61] [PID:2797] [RANK:0] total_num_tokens: 414041\u001b[39m\n",
206"[2024-01-27 22:06:25,001] [DEBUG] [axolotl.log:61] [PID:2797] [RANK:0] `total_supervised_tokens: 294246`\u001b[39m\n",
207"[2024-01-27 22:06:25,009] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 1.0 total_num_tokens per device: 414041\u001b[39m\n",
208"[2024-01-27 22:06:25,009] [DEBUG] [axolotl.log:61] [PID:2797] [RANK:0] data_loader_len: 372\u001b[39m\n",
209"[2024-01-27 22:06:25,009] [INFO] [axolotl.log:61] [PID:2797] [RANK:0] sample_packing_eff_est across ranks: [0.8624991667499917]\u001b[39m\n",
210"[2024-01-27 22:06:25,009] [DEBUG] [axolotl.log:61] [PID:2797] [RANK:0] sample_packing_eff_est: 0.87\u001b[39m\n",
211"[2024-01-27 22:06:25,009] [DEBUG] [axolotl.log:61] [PID:2797] [RANK:0] total_num_steps: 1488\u001b[39m\n",
212"[2024-01-27 22:06:25,009] [INFO] [axolotl.prepare_dataset:115] [PID:2797] [RANK:0] Maximum number of steps set at 20\u001b[39m\n",
213"[2024-01-27 22:06:25,010] [DEBUG] [axolotl.train.log:61] [PID:2797] [RANK:0] loading tokenizer... TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T\u001b[39m\n",
214"[2024-01-27 22:06:25,574] [DEBUG] [axolotl.load_tokenizer:216] [PID:2797] [RANK:0] EOS: 2 / </s>\u001b[39m\n",
215"[2024-01-27 22:06:25,575] [DEBUG] [axolotl.load_tokenizer:217] [PID:2797] [RANK:0] BOS: 1 / <s>\u001b[39m\n",
216"[2024-01-27 22:06:25,575] [DEBUG] [axolotl.load_tokenizer:218] [PID:2797] [RANK:0] PAD: 2 / </s>\u001b[39m\n",
217"[2024-01-27 22:06:25,575] [DEBUG] [axolotl.load_tokenizer:219] [PID:2797] [RANK:0] UNK: 0 / <unk>\u001b[39m\n",
218"[2024-01-27 22:06:25,575] [INFO] [axolotl.load_tokenizer:230] [PID:2797] [RANK:0] No Chat template selected. Consider adding a chat template for easier inference.\u001b[39m\n",
219"[2024-01-27 22:06:25,575] [DEBUG] [axolotl.train.log:61] [PID:2797] [RANK:0] loading model and peft_config...\u001b[39m\n",
220"[2024-01-27 22:06:25,678] [INFO] [axolotl.load_model:372] [PID:2797] [RANK:0] patching _expand_mask\u001b[39m\n",
221"model.safetensors: 100% 4.40G/4.40G [01:42<00:00, 42.8MB/s]\n",
222"generation_config.json: 100% 129/129 [00:00<00:00, 758kB/s]\n",
223"[2024-01-27 22:08:13,799] [INFO] [axolotl.load_model:641] [PID:2797] [RANK:0] GPU memory usage after model load: 0.753GB (+0.022GB cache, +0.368GB misc)\u001b[39m\n",
224"[2024-01-27 22:08:13,825] [INFO] [axolotl.load_model:673] [PID:2797] [RANK:0] converting PEFT model w/ prepare_model_for_kbit_training\u001b[39m\n",
225"[2024-01-27 22:08:13,829] [INFO] [axolotl.load_model:685] [PID:2797] [RANK:0] converting modules to torch.float16 for flash attention\u001b[39m\n",
226"[2024-01-27 22:08:13,832] [INFO] [axolotl.load_lora:797] [PID:2797] [RANK:0] found linear modules: ['v_proj', 'k_proj', 'gate_proj', 'up_proj', 'o_proj', 'q_proj', 'down_proj']\u001b[39m\n",
227"trainable params: 25,231,360 || all params: 1,125,279,744 || trainable%: 2.2422299996542017\n",
228"[2024-01-27 22:08:14,263] [INFO] [axolotl.load_model:722] [PID:2797] [RANK:0] GPU memory usage after adapters: 0.847GB (+0.514GB cache, +0.368GB misc)\u001b[39m\n",
229"[2024-01-27 22:08:14,273] [INFO] [axolotl.train.log:61] [PID:2797] [RANK:0] Pre-saving adapter config to ./qlora-out\u001b[39m\n",
230"[2024-01-27 22:08:14,280] [INFO] [axolotl.train.log:61] [PID:2797] [RANK:0] Starting trainer...\u001b[39m\n",
231"[2024-01-27 22:08:14,686] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 414041\u001b[39m\n",
232"[2024-01-27 22:08:14,688] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 414041\u001b[39m\n",
233"[2024-01-27 22:08:14,850] [INFO] [axolotl.callbacks.on_train_begin:601] [PID:2797] [RANK:0] The Axolotl config has been saved to the MLflow artifacts.\u001b[39m\n",
234" 0% 0/20 [00:00<?, ?it/s][2024-01-27 22:08:14,853] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 414041\u001b[39m\n",
235"{'loss': 1.6463, 'learning_rate': 2e-05, 'epoch': 0.0}\n",
236" 5% 1/20 [00:02<00:45, 2.41s/it][2024-01-27 22:08:18,019] [INFO] [axolotl.callbacks.on_step_end:125] [PID:2797] [RANK:0] GPU memory usage while training: 0.963GB (+0.805GB cache, +0.638GB misc)\u001b[39m\n",
237"{'loss': 4.8805, 'learning_rate': 4e-05, 'epoch': 0.0}\n",
238"{'loss': 4.4957, 'learning_rate': 6e-05, 'epoch': 0.01}\n",
239"{'loss': 4.4627, 'learning_rate': 8e-05, 'epoch': 0.01}\n",
240"{'loss': 4.8849, 'learning_rate': 0.0001, 'epoch': 0.01}\n",
241"{'loss': 5.0871, 'learning_rate': 0.00012, 'epoch': 0.01}\n",
242"{'loss': 3.0099, 'learning_rate': 0.00014, 'epoch': 0.02}\n",
243"{'loss': 4.4522, 'learning_rate': 0.00016, 'epoch': 0.02}\n",
244"{'loss': 2.2085, 'learning_rate': 0.00018, 'epoch': 0.02}\n",
245"{'loss': 4.4377, 'learning_rate': 0.0002, 'epoch': 0.02}\n",
246"{'loss': 3.7593, 'learning_rate': 0.00019510565162951537, 'epoch': 0.03}\n",
247"{'loss': 3.5486, 'learning_rate': 0.00018090169943749476, 'epoch': 0.03}\n",
248"{'loss': 3.7615, 'learning_rate': 0.00015877852522924732, 'epoch': 0.03}\n",
249"{'loss': 3.5778, 'learning_rate': 0.00013090169943749476, 'epoch': 0.03}\n",
250"{'loss': 3.3562, 'learning_rate': 0.0001, 'epoch': 0.04}\n",
251"{'loss': 3.7881, 'learning_rate': 6.909830056250527e-05, 'epoch': 0.04}\n",
252"{'loss': 3.4147, 'learning_rate': 4.12214747707527e-05, 'epoch': 0.04}\n",
253"{'loss': 3.7467, 'learning_rate': 1.9098300562505266e-05, 'epoch': 0.04}\n",
254"{'loss': 3.6867, 'learning_rate': 4.8943483704846475e-06, 'epoch': 0.04}\n",
255"{'loss': 4.0308, 'learning_rate': 0.0, 'epoch': 0.05}\n",
256"100% 20/20 [00:17<00:00, 1.26it/s][2024-01-27 22:08:32,098] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
257"[2024-01-27 22:08:32,232] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
258"[2024-01-27 22:08:32,232] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
259"\n",
260" 0% 0/22 [00:00<?, ?it/s]\u001b[A[2024-01-27 22:08:32,451] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
261"\n",
262" 9% 2/22 [00:00<00:02, 9.16it/s]\u001b[A[2024-01-27 22:08:32,669] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
263"\n",
264" 14% 3/22 [00:00<00:02, 6.46it/s]\u001b[A[2024-01-27 22:08:32,890] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
265"\n",
266" 18% 4/22 [00:00<00:03, 5.58it/s]\u001b[A[2024-01-27 22:08:33,119] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
267"\n",
268" 23% 5/22 [00:00<00:03, 5.08it/s]\u001b[A[2024-01-27 22:08:33,339] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
269"\n",
270" 27% 6/22 [00:01<00:03, 4.89it/s]\u001b[A[2024-01-27 22:08:33,559] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
271"\n",
272" 32% 7/22 [00:01<00:03, 4.78it/s]\u001b[A[2024-01-27 22:08:33,779] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
273"\n",
274" 36% 8/22 [00:01<00:02, 4.70it/s]\u001b[A[2024-01-27 22:08:34,004] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
275"\n",
276" 41% 9/22 [00:01<00:02, 4.62it/s]\u001b[A[2024-01-27 22:08:34,224] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
277"\n",
278" 45% 10/22 [00:01<00:02, 4.59it/s]\u001b[A[2024-01-27 22:08:34,447] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
279"\n",
280" 50% 11/22 [00:02<00:02, 4.56it/s]\u001b[A[2024-01-27 22:08:34,670] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
281"\n",
282" 55% 12/22 [00:02<00:02, 4.54it/s]\u001b[A[2024-01-27 22:08:34,889] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
283"\n",
284" 59% 13/22 [00:02<00:01, 4.55it/s]\u001b[A[2024-01-27 22:08:35,115] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
285"\n",
286" 64% 14/22 [00:02<00:01, 4.51it/s]\u001b[A[2024-01-27 22:08:35,337] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
287"\n",
288" 68% 15/22 [00:03<00:01, 4.51it/s]\u001b[A[2024-01-27 22:08:35,559] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
289"\n",
290" 73% 16/22 [00:03<00:01, 4.51it/s]\u001b[A[2024-01-27 22:08:35,783] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
291"\n",
292" 77% 17/22 [00:03<00:01, 4.49it/s]\u001b[A[2024-01-27 22:08:36,005] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
293"\n",
294" 82% 18/22 [00:03<00:00, 4.50it/s]\u001b[A[2024-01-27 22:08:36,228] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
295"\n",
296" 86% 19/22 [00:03<00:00, 4.50it/s]\u001b[A[2024-01-27 22:08:36,449] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
297"\n",
298" 91% 20/22 [00:04<00:00, 4.50it/s]\u001b[A[2024-01-27 22:08:36,676] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
299"\n",
300" 95% 21/22 [00:04<00:00, 4.47it/s]\u001b[A[2024-01-27 22:08:36,896] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
301"\n",
302"100% 22/22 [00:04<00:00, 4.50it/s]\u001b[A[2024-01-27 22:08:37,116] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
303"\n",
304"23it [00:04, 4.51it/s] \u001b[A[2024-01-27 22:08:37,336] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
305"\n",
306"24it [00:05, 4.52it/s]\u001b[A[2024-01-27 22:08:37,556] [INFO] [axolotl.utils.samplers.multipack._len_est:178] [PID:2797] [RANK:0] packing_efficiency_estimate: 0.87 total_num_tokens per device: 22777\u001b[39m\n",
307"\n",
308" \n",
309"\u001b[A{'eval_loss': 3.513343572616577, 'eval_runtime': 5.5482, 'eval_samples_per_second': 18.024, 'eval_steps_per_second': 18.024, 'epoch': 0.05}\n",
310"100% 20/20 [00:22<00:00, 1.26it/s]\n",
311"25it [00:05, 4.53it/s]\u001b[A\n",
312"{'train_runtime': 24.8459, 'train_samples_per_second': 0.805, 'train_steps_per_second': 0.805, 'train_loss': 3.8117882788181303, 'epoch': 0.05}\n",
313"100% 20/20 [00:24<00:00, 1.24s/it]\n",
314"[2024-01-27 22:08:39,580] [INFO] [axolotl.train.log:61] [PID:2797] [RANK:0] Training Completed!!! Saving pre-trained model to ./qlora-out\u001b[39m\n",
315"(PeftModelForCausalLM( (base_model): LoraModel( (model): LlamaForCausalLM( (model): LlamaModel( (embed_tokens): Embedding(32000, 2048) (layers): ModuleList( (0-21): 22 x LlamaDecoderLayer( (self_attn): LlamaSdpaAttention( (q_proj): lora.Linear4bit( (base_layer): Linear4bit(in_features=2048, out_features=2048, bias=False) (lora_dropout): ModuleDict( (default): Dropout(p=0.05, inplace=False) ) (lora_A): ModuleDict( (default): Linear(in_features=2048, out_features=32, bias=False) ) (lora_B): ModuleDict( (default): Linear(in_features=32, out_features=2048, bias=False) ) (lora_embedding_A): ParameterDict() (lora_embedding_B): ParameterDict() ) (k_proj): lora.Linear4bit( (base_layer): Linear4bit(in_features=2048, out_features=256, bias=False) (lora_dropout): ModuleDict( (default): Dropout(p=0.05, inplace=False) ) (lora_A): ModuleDict( (default): Linear(in_features=2048, out_features=32, bias=False) ) (lora_B): ModuleDict( (default): Linear(in_features=32, out_features=256, bias=False) ) (lora_embedding_A): ParameterDict() (lora_embedding_B): ParameterDict() ) (v_proj): lora.Linear4bit( (base_layer): Linear4bit(in_features=2048, out_features=256, bias=False) (lora_dropout): ModuleDict( (default): Dropout(p=0.05, inplace=False) ) (lora_A): ModuleDict( (default): Linear(in_features=2048, out_features=32, bias=False) ) (lora_B): ModuleDict( (default): Linear(in_features=32, out_features=256, bias=False) ) (lora_embedding_A): ParameterDict() (lora_embedding_B): ParameterDict() ) (o_proj): lora.Linear4bit( (base_layer): Linear4bit(in_features=2048, out_features=2048, bias=False) (lora_dropout): ModuleDict( (default): Dropout(p=0.05, inplace=False) ) (lora_A): ModuleDict( (default): Linear(in_features=2048, out_features=32, bias=False) ) (lora_B): ModuleDict( (default): Linear(in_features=32, out_features=2048, bias=False) ) (lora_embedding_A): ParameterDict() (lora_embedding_B): ParameterDict() ) (rotary_emb): LlamaRotaryEmbedding() ) (mlp): LlamaMLP( (gate_proj): lora.Linear4bit( (base_layer): Linear4bit(in_features=2048, out_features=5632, bias=False) (lora_dropout): ModuleDict( (default): Dropout(p=0.05, inplace=False) ) (lora_A): ModuleDict( (default): Linear(in_features=2048, out_features=32, bias=False) ) (lora_B): ModuleDict( (default): Linear(in_features=32, out_features=5632, bias=False) ) (lora_embedding_A): ParameterDict() (lora_embedding_B): ParameterDict() ) (up_proj): lora.Linear4bit( (base_layer): Linear4bit(in_features=2048, out_features=5632, bias=False) (lora_dropout): ModuleDict( (default): Dropout(p=0.05, inplace=False) ) (lora_A): ModuleDict( (default): Linear(in_features=2048, out_features=32, bias=False) ) (lora_B): ModuleDict( (default): Linear(in_features=32, out_features=5632, bias=False) ) (lora_embedding_A): ParameterDict() (lora_embedding_B): ParameterDict() ) (down_proj): lora.Linear4bit( (base_layer): Linear4bit(in_features=5632, out_features=2048, bias=False) (lora_dropout): ModuleDict( (default): Dropout(p=0.05, inplace=False) ) (lora_A): ModuleDict( (default): Linear(in_features=5632, out_features=32, bias=False) ) (lora_B): ModuleDict( (default): Linear(in_features=32, out_features=2048, bias=False) ) (lora_embedding_A): ParameterDict() (lora_embedding_B): ParameterDict() ) (act_fn): SiLU() ) (input_layernorm): LlamaRMSNorm() (post_attention_layernorm): LlamaRMSNorm() ) ) (norm): LlamaRMSNorm() ) (lm_head): Linear(in_features=2048, out_features=32000, bias=False) ) ) ), LlamaTokenizer(name_or_path='TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T', vocab_size=32000, model_max_length=1000000000000000019884624838656, is_fast=False, padding_side='right', truncation_side='right', special_tokens={'bos_token': '<s>', 'eos_token': '</s>', 'unk_token': '<unk>', 'pad_token': '</s>'}, clean_up_tokenization_spaces=False), added_tokens_decoder={ \t0: AddedToken(\"<unk>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True), \t1: AddedToken(\"<s>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True), \t2: AddedToken(\"</s>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True), })\n",
316"\u001b[0m"
317]
318}
319],
320"source": [
321"!accelerate launch -m axolotl.cli.train config.yaml"
322]
323},
324{
325"cell_type": "code",
326"source": [
327"!python3 -m axolotl.cli.merge_lora config.yaml --lora_model_dir=\"./qlora-out\""
328],
329"metadata": {
330"colab": {
331"base_uri": "https://localhost:8080/"
332},
333"id": "5GENnc9Z8v-P",
334"outputId": "762e6290-9877-485c-c084-5370c3b3d7f3"
335},
336"execution_count": 10,
337"outputs": [
338{
339"output_type": "stream",
340"name": "stdout",
341"text": [
342"mkdir: cannot create directory ‘final_model’: File exists\n",
343"2024-01-27 22:10:36.876247: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
344"2024-01-27 22:10:36.876317: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
345"2024-01-27 22:10:36.878122: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
346"2024-01-27 22:10:38.390922: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
347"[2024-01-27 22:10:39,447] [INFO] [datasets.<module>:58] [PID:4079] PyTorch version 2.1.2 available.\n",
348"[2024-01-27 22:10:39,448] [INFO] [datasets.<module>:95] [PID:4079] TensorFlow version 2.15.0 available.\n",
349"[2024-01-27 22:10:39,449] [INFO] [datasets.<module>:108] [PID:4079] JAX version 0.4.23 available.\n",
350"[2024-01-27 22:10:40,350] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n",
351" dP dP dP \n",
352" 88 88 88 \n",
353" .d8888b. dP. .dP .d8888b. 88 .d8888b. d8888P 88 \n",
354" 88' `88 `8bd8' 88' `88 88 88' `88 88 88 \n",
355" 88. .88 .d88b. 88. .88 88 88. .88 88 88 \n",
356" `88888P8 dP' `dP `88888P' dP `88888P' dP dP \n",
357" \n",
358" \n",
359"\n",
360"[2024-01-27 22:10:41,928] [INFO] [axolotl.normalize_config:170] [PID:4079] [RANK:0] GPU memory usage baseline: 0.000GB (+0.255GB misc)\u001b[39m\n",
361"[2024-01-27 22:10:41,928] [INFO] [axolotl.common.cli.load_model_and_tokenizer:49] [PID:4079] [RANK:0] loading tokenizer... TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T\u001b[39m\n",
362"[2024-01-27 22:10:42,234] [DEBUG] [axolotl.load_tokenizer:216] [PID:4079] [RANK:0] EOS: 2 / </s>\u001b[39m\n",
363"[2024-01-27 22:10:42,234] [DEBUG] [axolotl.load_tokenizer:217] [PID:4079] [RANK:0] BOS: 1 / <s>\u001b[39m\n",
364"[2024-01-27 22:10:42,234] [DEBUG] [axolotl.load_tokenizer:218] [PID:4079] [RANK:0] PAD: 2 / </s>\u001b[39m\n",
365"[2024-01-27 22:10:42,234] [DEBUG] [axolotl.load_tokenizer:219] [PID:4079] [RANK:0] UNK: 0 / <unk>\u001b[39m\n",
366"[2024-01-27 22:10:42,235] [INFO] [axolotl.load_tokenizer:230] [PID:4079] [RANK:0] No Chat template selected. Consider adding a chat template for easier inference.\u001b[39m\n",
367"[2024-01-27 22:10:42,235] [INFO] [axolotl.common.cli.load_model_and_tokenizer:51] [PID:4079] [RANK:0] loading model and (optionally) peft_config...\u001b[39m\n",
368"[2024-01-27 22:10:42,320] [INFO] [axolotl.load_model:372] [PID:4079] [RANK:0] patching _expand_mask\u001b[39m\n",
369"[2024-01-27 22:10:45,770] [INFO] [axolotl.load_model:641] [PID:4079] [RANK:0] GPU memory usage after model load: 2.062GB (+0.087GB cache, +0.352GB misc)\u001b[39m\n",
370"[2024-01-27 22:10:45,787] [INFO] [axolotl.load_model:685] [PID:4079] [RANK:0] converting modules to torch.float16 for flash attention\u001b[39m\n",
371"[2024-01-27 22:10:45,791] [INFO] [axolotl.load_lora:797] [PID:4079] [RANK:0] found linear modules: ['q_proj', 'down_proj', 'v_proj', 'gate_proj', 'k_proj', 'up_proj', 'o_proj']\u001b[39m\n",
372"[2024-01-27 22:10:45,791] [DEBUG] [axolotl.load_lora:816] [PID:4079] [RANK:0] Loading pretained PEFT - LoRA\u001b[39m\n",
373"trainable params: 25,231,360 || all params: 1,125,279,744 || trainable%: 2.2422299996542017\n",
374"[2024-01-27 22:10:46,372] [INFO] [axolotl.load_model:722] [PID:4079] [RANK:0] GPU memory usage after adapters: 2.590GB (+1.060GB cache, +0.368GB misc)\u001b[39m\n",
375"[2024-01-27 22:10:46,372] [INFO] [axolotl.scripts.do_merge_lora:79] [PID:4079] [RANK:0] running merge of LoRA with base model\u001b[39m\n",
376"Unloading and merging model: 100% 468/468 [00:00<00:00, 4211.50it/s]\n",
377"[2024-01-27 22:10:46,490] [INFO] [axolotl.scripts.do_merge_lora:88] [PID:4079] [RANK:0] saving merged model to: qlora-out/merged\u001b[39m\n",
378"\u001b[0m"
379]
380}
381]
382},
383{
384"cell_type": "code",
385"source": [
386"from huggingface_hub import HfApi\n",
387"from google.colab import userdata\n",
388"\n",
389"new_model = \"mlabonne/TinyAlpaca\"\n",
390"\n",
391"# HF_TOKEN defined in the secrets tab in Google Colab\n",
392"api = HfApi()\n",
393"\n",
394"# Upload merge folder\n",
395"api.create_repo(\n",
396" repo_id=new_model,\n",
397" repo_type=\"model\",\n",
398" exist_ok=True,\n",
399")\n",
400"api.upload_folder(\n",
401" repo_id=new_model,\n",
402" folder_path=\"qlora-out/merged\",\n",
403")"
404],
405"metadata": {
406"colab": {
407"base_uri": "https://localhost:8080/",
408"height": 149,
409"referenced_widgets": [
410"76eab4e675b34a5486f28d16183658b6",
411"af7f38e810a64e5f98dd3f33672b8d57",
412"0e196628e7e64879bcc7184f67404127",
413"2ee0d001c05a4b89ab7a3cbea71c5960",
414"8a59f96ead334ba8b6848321fbde0811",
415"3db6a3ae89ec483783f97d77aef5e5a7",
416"f589d24de591428cb878405d1eb6034d",
417"e4520b22ca934c1fbe399131c021d0e8",
418"cb663cc62fc94108855d9b5509b573d8",
419"9926acc37862427d9fdd1ef37474d0c9",
420"215f84842ac5451a8f868e0dc3090a71",
421"0878f651a6b64ef495ebd5a2a9084b63",
422"eec99342bca04c1697ec43bc099c22c9",
423"cb7cc89f3edf4c6c8e38043ef0d697ba",
424"7a9497ed06004a82b2893e0f5ab03736",
425"f50ea1d3c5874926a6db2af3d9d795ab",
426"7851c929f5f64e31856d3793b963130e",
427"49f8123c3c5e43ababed7e09e97cb7d2",
428"c4dfec3f687241d6a69cf30252ada077",
429"a1c9ecf2bc124658a08f585b1bf309e6",
430"101d0b228b1943ff9c9d69fd6f4847ee",
431"b03c7fa74c774a39946636803e086218",
432"90726b0049c344c19fb5c9ef1dcf0628",
433"bebef38ee9e24586a7f894ffaf1ae43e",
434"9d90c5ea423e4586bc3bb303bcaa4b03",
435"d417d5bf00294e3f9cb7e78b5d83ee03",
436"68eac408135d4d048de89fa674b701da",
437"34c11d3b1a3441bf8533f7a6f057d0cf",
438"6270a81b7f5749d7a656c1edaaa63118",
439"e1de7a96086c4351bba99bbd4d905802",
440"6d28893d0a054d6aa2726ee94ee0d880",
441"91c9822b8ef14074bd360bb5d2998267",
442"8673edee8ab5430588daba8d422999eb"
443]
444},
445"id": "NsvZw3B7-gfm",
446"outputId": "68c10dec-ee7a-4c0d-dd8d-c6631b53f4b3"
447},
448"execution_count": 13,
449"outputs": [
450{
451"output_type": "display_data",
452"data": {
453"text/plain": [
454"pytorch_model.bin: 0%| | 0.00/2.20G [00:00<?, ?B/s]"
455],
456"application/vnd.jupyter.widget-view+json": {
457"version_major": 2,
458"version_minor": 0,
459"model_id": "76eab4e675b34a5486f28d16183658b6"
460}
461},
462"metadata": {}
463},
464{
465"output_type": "display_data",
466"data": {
467"text/plain": [
468"Upload 2 LFS files: 0%| | 0/2 [00:00<?, ?it/s]"
469],
470"application/vnd.jupyter.widget-view+json": {
471"version_major": 2,
472"version_minor": 0,
473"model_id": "0878f651a6b64ef495ebd5a2a9084b63"
474}
475},
476"metadata": {}
477},
478{
479"output_type": "display_data",
480"data": {
481"text/plain": [
482"tokenizer.model: 0%| | 0.00/500k [00:00<?, ?B/s]"
483],
484"application/vnd.jupyter.widget-view+json": {
485"version_major": 2,
486"version_minor": 0,
487"model_id": "90726b0049c344c19fb5c9ef1dcf0628"
488}
489},
490"metadata": {}
491},
492{
493"output_type": "execute_result",
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