317 lines
7.4 KiB
Plaintext
317 lines
7.4 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "d4c7c96e",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"%pip install transformers>=4.51.0"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "3edd43ac",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.\n",
|
|
"%pip install torch"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "d41f8851",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"%pip install unsloth"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "1b5a6da6",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"%pip install datasets"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "d5256285",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from unsloth import FastLanguageModel, is_bfloat16_supported"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "88abe86a",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from transformers import AutoTokenizer, TrainingArguments\n",
|
|
"from trl import SFTTrainer, DataCollatorForCompletionOnlyLM"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "b404a9db",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import dataset"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "01519eee",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"model_name = \"unsloth/Qwen3-0.6B-unsloth-bnb-4bit\"\n",
|
|
"#model_name = \"Qwen/Qwen3-0.6B\""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "04e1aad4",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"\n",
|
|
"# load the tokenizer and the model\n",
|
|
"tokenizer = AutoTokenizer.from_pretrained(model_name)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "31f2451c",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"model, _returned_tokenizer = FastLanguageModel.from_pretrained(\n",
|
|
" model_name,\n",
|
|
" gpu_memory_utilization = 0.5, # Reduce if out of memory\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "2dddcb05",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# prepare the model input\n",
|
|
"prompt = \"Give me a short introduction to large language model.\"\n",
|
|
"messages = [\n",
|
|
" {\"role\": \"user\", \"content\": prompt}\n",
|
|
"]\n",
|
|
"text = tokenizer.apply_chat_template(\n",
|
|
" messages,\n",
|
|
" tokenize=False,\n",
|
|
" add_generation_prompt=True,\n",
|
|
" enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.\n",
|
|
")\n",
|
|
"model_inputs = tokenizer([text], return_tensors=\"pt\").to(model.device)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "84a7b4cc",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"## conduct text completion\n",
|
|
"#generated_ids = model.generate(\n",
|
|
"# **model_inputs,\n",
|
|
"# max_new_tokens=32768\n",
|
|
"#)\n",
|
|
"#output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "05be1a53",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"## parsing thinking content\n",
|
|
"#try:\n",
|
|
"# # rindex finding 151668 (</think>)\n",
|
|
"# index = len(output_ids) - output_ids[::-1].index(151668)\n",
|
|
"#except ValueError:\n",
|
|
"# index = 0"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "64775c31",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip(\"\\n\")\n",
|
|
"#content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip(\"\\n\")\n",
|
|
"#\n",
|
|
"#print(\"thinking content:\", thinking_content)\n",
|
|
"#print(\"content:\", content)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "48477dfd",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"dataset = dataset.Dataset(\"/root/sia/training/config.yaml\")\n",
|
|
"dataset.validate()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "90b56737",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"model = FastLanguageModel.get_peft_model(\n",
|
|
" model,\n",
|
|
" target_modules = [\n",
|
|
" \"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n",
|
|
" \"gate_proj\", \"up_proj\", \"down_proj\",\n",
|
|
" ],\n",
|
|
" lora_dropout = 0, # Supports any, but = 0 is optimized\n",
|
|
" bias = \"none\", # Supports any, but = \"none\" is optimized\n",
|
|
" use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n",
|
|
" random_state = 3407,\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "21422429",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"training_args = TrainingArguments(\n",
|
|
" output_dir=\"/root/models/qwen_train\",\n",
|
|
" num_train_epochs=3,\n",
|
|
" per_device_train_batch_size=1,\n",
|
|
" gradient_accumulation_steps=16,\n",
|
|
" gradient_checkpointing=True,\n",
|
|
" learning_rate=2e-5,\n",
|
|
" lr_scheduler_type=\"cosine\",\n",
|
|
" warmup_ratio=0.05,\n",
|
|
" weight_decay=0.01,\n",
|
|
" fp16=not is_bfloat16_supported(),\n",
|
|
" bf16=is_bfloat16_supported(),\n",
|
|
" logging_steps=10,\n",
|
|
" save_steps=200,\n",
|
|
" save_total_limit=3,\n",
|
|
" report_to=\"none\",\n",
|
|
" optim=\"adamw_8bit\",\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "83be036e",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"trainer = SFTTrainer(\n",
|
|
" model=model,\n",
|
|
" tokenizer=tokenizer,\n",
|
|
" args=training_args,\n",
|
|
" train_dataset=dataset.to_transformers_dataset(tokenizer),\n",
|
|
" dataset_text_field=\"messages\",\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "886ba936",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"trainer.train()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "13071f43",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"model.save_pretrained_merged(\n",
|
|
" \"/root/models/qwen_merged_4bit\", \n",
|
|
" tokenizer=tokenizer,\n",
|
|
" save_method=\"merged_4bit_forced\"\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "a80162e1",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"%pip install vllm"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "9a934ce7",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"%vllm serve /root/models/qwen_merged_4bit --enable-reasoning --reasoning-parser deepseek_r1"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "notebook",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.10.12"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|