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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"gpuType": "T4"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "Hyg4prvKsTfC"
},
"outputs": [],
"source": [
"#!pip install bitsandbytes\n"
]
},
{
"cell_type": "code",
"source": [
"#pip install accelerate"
],
"metadata": {
"id": "MVqLMtMt0Uhc"
},
"execution_count": 15,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#!pip install accelerate\n",
"#!pip install bitsandbytes -i https://pypi.org/simple/\n"
],
"metadata": {
"id": "BeHjgLP_0laH"
},
"execution_count": 16,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Load model directly\n",
"from transformers import AutoTokenizer, AutoModelForCausalLM\n",
"\n",
"#tokenizer = AutoTokenizer.from_pretrained(\"harry85/tokenizer-finetuned-TinyLLAMA\")\n",
"#model = AutoModelForCausalLM.from_pretrained(\"unsloth/tinyllama-bnb-4bit\")\n",
"# Load model directly\n",
"#from transformers import AutoTokenizer, AutoModelForCausalLM\n",
"# Load model directly\n",
"from transformers import AutoTokenizer, AutoModelForCausalLM\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(\"harry85/finetuned-TinyLLAMA-own-data-07\")\n",
"model = AutoModelForCausalLM.from_pretrained(\"harry85/finetuned-TinyLLAMA-own-data-07\")\n",
"\n",
"\n",
"# Enable native 2x faster inference if supported\n",
"# This feature depends on the specific model and framework used; modify as needed.\n",
"# For example, in the case of some models, you can use model.half() to convert to FP16 for faster inference.\n",
"\n",
"# Define the Alpaca prompt\n",
"alpaca_prompt = \"\"\"\\\n",
"### Instruction:\n",
"{0}\n",
"\n",
"### Input:\n",
"\n",
"{1}\n",
"\n",
"### Response:\n",
"{2}\"\"\"\n",
"\n",
"# Prepare the input\n",
"inputs = tokenizer(\n",
" [\n",
" alpaca_prompt.format(\n",
" \"Continue the Fibonacci sequence.\", # instruction\n",
" \"1, 1, 2, 3, 5, 8\", # input\n",
" \"\" # output - leave this blank for generation!\n",
" )\n",
" ],\n",
" return_tensors=\"pt\"\n",
").to(\"cuda\")\n",
"\n",
"# Generate the output\n",
"outputs = model.generate(**inputs, max_new_tokens=64, use_cache=True)\n",
"\n",
"# Decode and print the output\n",
"response = tokenizer.batch_decode(outputs, skip_special_tokens=True)\n",
"print(response)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "RxlJ3o75sUrN",
"outputId": "1c767815-7def-42a3-f250-69c7bbe802b7"
},
"execution_count": 24,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Unused kwargs: ['quant_method']. These kwargs are not used in <class 'transformers.utils.quantization_config.BitsAndBytesConfig'>.\n",
"`low_cpu_mem_usage` was None, now set to True since model is quantized.\n",
"Some weights of the model checkpoint at harry85/finetuned-TinyLLAMA-own-data-07 were not used when initializing LlamaForCausalLM: ['base_model.model.model.layers.0.mlp.down_proj.lora_A.weight', 'base_model.model.model.layers.0.mlp.down_proj.lora_B.weight', 'base_model.model.model.layers.0.mlp.gate_proj.lora_A.weight', 'base_model.model.model.layers.0.mlp.gate_proj.lora_B.weight', 'base_model.model.model.layers.0.mlp.up_proj.lora_A.weight', 'base_model.model.model.layers.0.mlp.up_proj.lora_B.weight', 'base_model.model.model.layers.0.self_attn.k_proj.lora_A.weight', 'base_model.model.model.layers.0.self_attn.k_proj.lora_B.weight', 'base_model.model.model.layers.0.self_attn.o_proj.lora_A.weight', 'base_model.model.model.layers.0.self_attn.o_proj.lora_B.weight', 'base_model.model.model.layers.0.self_attn.q_proj.lora_A.weight', 'base_model.model.model.layers.0.self_attn.q_proj.lora_B.weight', 'base_model.model.model.layers.0.self_attn.v_proj.lora_A.weight', 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'base_model.model.model.layers.8.mlp.gate_proj.lora_B.weight', 'base_model.model.model.layers.8.mlp.up_proj.lora_A.weight', 'base_model.model.model.layers.8.mlp.up_proj.lora_B.weight', 'base_model.model.model.layers.8.self_attn.k_proj.lora_A.weight', 'base_model.model.model.layers.8.self_attn.k_proj.lora_B.weight', 'base_model.model.model.layers.8.self_attn.o_proj.lora_A.weight', 'base_model.model.model.layers.8.self_attn.o_proj.lora_B.weight', 'base_model.model.model.layers.8.self_attn.q_proj.lora_A.weight', 'base_model.model.model.layers.8.self_attn.q_proj.lora_B.weight', 'base_model.model.model.layers.8.self_attn.v_proj.lora_A.weight', 'base_model.model.model.layers.8.self_attn.v_proj.lora_B.weight', 'base_model.model.model.layers.9.mlp.down_proj.lora_A.weight', 'base_model.model.model.layers.9.mlp.down_proj.lora_B.weight', 'base_model.model.model.layers.9.mlp.gate_proj.lora_A.weight', 'base_model.model.model.layers.9.mlp.gate_proj.lora_B.weight', 'base_model.model.model.layers.9.mlp.up_proj.lora_A.weight', 'base_model.model.model.layers.9.mlp.up_proj.lora_B.weight', 'base_model.model.model.layers.9.self_attn.k_proj.lora_A.weight', 'base_model.model.model.layers.9.self_attn.k_proj.lora_B.weight', 'base_model.model.model.layers.9.self_attn.o_proj.lora_A.weight', 'base_model.model.model.layers.9.self_attn.o_proj.lora_B.weight', 'base_model.model.model.layers.9.self_attn.q_proj.lora_A.weight', 'base_model.model.model.layers.9.self_attn.q_proj.lora_B.weight', 'base_model.model.model.layers.9.self_attn.v_proj.lora_A.weight', 'base_model.model.model.layers.9.self_attn.v_proj.lora_B.weight']\n",
"- This IS expected if you are initializing LlamaForCausalLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
"- This IS NOT expected if you are initializing LlamaForCausalLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
"Some weights of LlamaForCausalLM were not initialized from the model checkpoint at harry85/finetuned-TinyLLAMA-own-data-07 and are newly initialized: ['embed_tokens.weight', 'layers.0.input_layernorm.weight', 'layers.0.mlp.down_proj.weight', 'layers.0.mlp.gate_proj.weight', 'layers.0.mlp.up_proj.weight', 'layers.0.post_attention_layernorm.weight', 'layers.0.self_attn.k_proj.weight', 'layers.0.self_attn.o_proj.weight', 'layers.0.self_attn.q_proj.weight', 'layers.0.self_attn.v_proj.weight', 'layers.1.input_layernorm.weight', 'layers.1.mlp.down_proj.weight', 'layers.1.mlp.gate_proj.weight', 'layers.1.mlp.up_proj.weight', 'layers.1.post_attention_layernorm.weight', 'layers.1.self_attn.k_proj.weight', 'layers.1.self_attn.o_proj.weight', 'layers.1.self_attn.q_proj.weight', 'layers.1.self_attn.v_proj.weight', 'layers.10.input_layernorm.weight', 'layers.10.mlp.down_proj.weight', 'layers.10.mlp.gate_proj.weight', 'layers.10.mlp.up_proj.weight', 'layers.10.post_attention_layernorm.weight', 'layers.10.self_attn.k_proj.weight', 'layers.10.self_attn.o_proj.weight', 'layers.10.self_attn.q_proj.weight', 'layers.10.self_attn.v_proj.weight', 'layers.11.input_layernorm.weight', 'layers.11.mlp.down_proj.weight', 'layers.11.mlp.gate_proj.weight', 'layers.11.mlp.up_proj.weight', 'layers.11.post_attention_layernorm.weight', 'layers.11.self_attn.k_proj.weight', 'layers.11.self_attn.o_proj.weight', 'layers.11.self_attn.q_proj.weight', 'layers.11.self_attn.v_proj.weight', 'layers.12.input_layernorm.weight', 'layers.12.mlp.down_proj.weight', 'layers.12.mlp.gate_proj.weight', 'layers.12.mlp.up_proj.weight', 'layers.12.post_attention_layernorm.weight', 'layers.12.self_attn.k_proj.weight', 'layers.12.self_attn.o_proj.weight', 'layers.12.self_attn.q_proj.weight', 'layers.12.self_attn.v_proj.weight', 'layers.13.input_layernorm.weight', 'layers.13.mlp.down_proj.weight', 'layers.13.mlp.gate_proj.weight', 'layers.13.mlp.up_proj.weight', 'layers.13.post_attention_layernorm.weight', 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'layers.16.self_attn.k_proj.weight', 'layers.16.self_attn.o_proj.weight', 'layers.16.self_attn.q_proj.weight', 'layers.16.self_attn.v_proj.weight', 'layers.17.input_layernorm.weight', 'layers.17.mlp.down_proj.weight', 'layers.17.mlp.gate_proj.weight', 'layers.17.mlp.up_proj.weight', 'layers.17.post_attention_layernorm.weight', 'layers.17.self_attn.k_proj.weight', 'layers.17.self_attn.o_proj.weight', 'layers.17.self_attn.q_proj.weight', 'layers.17.self_attn.v_proj.weight', 'layers.18.input_layernorm.weight', 'layers.18.mlp.down_proj.weight', 'layers.18.mlp.gate_proj.weight', 'layers.18.mlp.up_proj.weight', 'layers.18.post_attention_layernorm.weight', 'layers.18.self_attn.k_proj.weight', 'layers.18.self_attn.o_proj.weight', 'layers.18.self_attn.q_proj.weight', 'layers.18.self_attn.v_proj.weight', 'layers.19.input_layernorm.weight', 'layers.19.mlp.down_proj.weight', 'layers.19.mlp.gate_proj.weight', 'layers.19.mlp.up_proj.weight', 'layers.19.post_attention_layernorm.weight', 'layers.19.self_attn.k_proj.weight', 'layers.19.self_attn.o_proj.weight', 'layers.19.self_attn.q_proj.weight', 'layers.19.self_attn.v_proj.weight', 'layers.2.input_layernorm.weight', 'layers.2.mlp.down_proj.weight', 'layers.2.mlp.gate_proj.weight', 'layers.2.mlp.up_proj.weight', 'layers.2.post_attention_layernorm.weight', 'layers.2.self_attn.k_proj.weight', 'layers.2.self_attn.o_proj.weight', 'layers.2.self_attn.q_proj.weight', 'layers.2.self_attn.v_proj.weight', 'layers.20.input_layernorm.weight', 'layers.20.mlp.down_proj.weight', 'layers.20.mlp.gate_proj.weight', 'layers.20.mlp.up_proj.weight', 'layers.20.post_attention_layernorm.weight', 'layers.20.self_attn.k_proj.weight', 'layers.20.self_attn.o_proj.weight', 'layers.20.self_attn.q_proj.weight', 'layers.20.self_attn.v_proj.weight', 'layers.21.input_layernorm.weight', 'layers.21.mlp.down_proj.weight', 'layers.21.mlp.gate_proj.weight', 'layers.21.mlp.up_proj.weight', 'layers.21.post_attention_layernorm.weight', 'layers.21.self_attn.k_proj.weight', 'layers.21.self_attn.o_proj.weight', 'layers.21.self_attn.q_proj.weight', 'layers.21.self_attn.v_proj.weight', 'layers.3.input_layernorm.weight', 'layers.3.mlp.down_proj.weight', 'layers.3.mlp.gate_proj.weight', 'layers.3.mlp.up_proj.weight', 'layers.3.post_attention_layernorm.weight', 'layers.3.self_attn.k_proj.weight', 'layers.3.self_attn.o_proj.weight', 'layers.3.self_attn.q_proj.weight', 'layers.3.self_attn.v_proj.weight', 'layers.4.input_layernorm.weight', 'layers.4.mlp.down_proj.weight', 'layers.4.mlp.gate_proj.weight', 'layers.4.mlp.up_proj.weight', 'layers.4.post_attention_layernorm.weight', 'layers.4.self_attn.k_proj.weight', 'layers.4.self_attn.o_proj.weight', 'layers.4.self_attn.q_proj.weight', 'layers.4.self_attn.v_proj.weight', 'layers.5.input_layernorm.weight', 'layers.5.mlp.down_proj.weight', 'layers.5.mlp.gate_proj.weight', 'layers.5.mlp.up_proj.weight', 'layers.5.post_attention_layernorm.weight', 'layers.5.self_attn.k_proj.weight', 'layers.5.self_attn.o_proj.weight', 'layers.5.self_attn.q_proj.weight', 'layers.5.self_attn.v_proj.weight', 'layers.6.input_layernorm.weight', 'layers.6.mlp.down_proj.weight', 'layers.6.mlp.gate_proj.weight', 'layers.6.mlp.up_proj.weight', 'layers.6.post_attention_layernorm.weight', 'layers.6.self_attn.k_proj.weight', 'layers.6.self_attn.o_proj.weight', 'layers.6.self_attn.q_proj.weight', 'layers.6.self_attn.v_proj.weight', 'layers.7.input_layernorm.weight', 'layers.7.mlp.down_proj.weight', 'layers.7.mlp.gate_proj.weight', 'layers.7.mlp.up_proj.weight', 'layers.7.post_attention_layernorm.weight', 'layers.7.self_attn.k_proj.weight', 'layers.7.self_attn.o_proj.weight', 'layers.7.self_attn.q_proj.weight', 'layers.7.self_attn.v_proj.weight', 'layers.8.input_layernorm.weight', 'layers.8.mlp.down_proj.weight', 'layers.8.mlp.gate_proj.weight', 'layers.8.mlp.up_proj.weight', 'layers.8.post_attention_layernorm.weight', 'layers.8.self_attn.k_proj.weight', 'layers.8.self_attn.o_proj.weight', 'layers.8.self_attn.q_proj.weight', 'layers.8.self_attn.v_proj.weight', 'layers.9.input_layernorm.weight', 'layers.9.mlp.down_proj.weight', 'layers.9.mlp.gate_proj.weight', 'layers.9.mlp.up_proj.weight', 'layers.9.post_attention_layernorm.weight', 'layers.9.self_attn.k_proj.weight', 'layers.9.self_attn.o_proj.weight', 'layers.9.self_attn.q_proj.weight', 'layers.9.self_attn.v_proj.weight', 'lm_head.weight', 'norm.weight']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"['### Instruction:\\nContinue the Fibonacci sequence.\\n\\n### Input:\\n\\n1, 1, 2, 3, 5, 8\\n\\n### Response:\\n']\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"if False:\n",
" from unsloth import FastLanguageModel\n",
" model, tokenizer = FastLanguageModel.from_pretrained(\n",
" model_name = \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n",
" max_seq_length = max_seq_length,\n",
" dtype = dtype,\n",
" load_in_4bit = load_in_4bit,\n",
" )\n",
" FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
"\n",
"# alpaca_prompt = You MUST copy from above!\n",
"\n",
"inputs = tokenizer(\n",
"[\n",
" alpaca_prompt.format(\n",
" \"which country Haris Hota live\", # instruction\n",
" \"Haris Hota\", # input\n",
" \"\", # output - leave this blank for generation!\n",
" )\n",
"], return_tensors = \"pt\").to(\"cuda\")\n",
"\n",
"from transformers import TextStreamer\n",
"text_streamer = TextStreamer(tokenizer)\n",
"_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 64)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "IuUufGQz5BBR",
"outputId": "07f59e9c-a09e-484c-df1f-406f6e75e36b"
},
"execution_count": 26,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<s> ### Instruction:\n",
"which country Haris Hota live\n",
"\n",
"### Input:\n",
"\n",
"Haris Hota\n",
"\n",
"### Response:\n",
"<unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk><unk>\n"
]
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "aTqhZ5y1533Y"
},
"execution_count": null,
"outputs": []
}
]
} |