styalai/competition-math-phinetune-v1-1

Model Trained Using AutoTrain

This model was trained using AutoTrain. For more information, please visit AutoTrain.

Model Trained Using AutoTrain

This model was trained using AutoTrain. For more information, please visit AutoTrain.

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

torch.random.manual_seed(0)

model = AutoModelForCausalLM.from_pretrained(
    "styalai/competition-math-phinetune-v1", q
    device_map="cuda", 
    torch_dtype="auto", 
    trust_remote_code=True, 
)
tokenizer = AutoTokenizer.from_pretrained("styalai/competition-math-phinetune-v1")

messages = [
    {"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"},
]

pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
)

generation_args = {
    "max_new_tokens": 500,
    "return_full_text": False,
    "temperature": 0.0,
    "do_sample": False,
}

output = pipe(messages, **generation_args)
print(output[0]['generated_text'])

Info

Fine-tune from styalai/phi-ne-tuning-1-4 who it fine tune from phi-3

parameters of autotrain :

project_name = 'competition-math-phinetune-v1-1' # @param {type:"string"}
model_name = "styalai/competition-math-phinetune-v1" #'microsoft/Phi-3-mini-4k-instruct' # @param {type:"string"}

#@markdown ---
#@markdown #### Push to Hub?
#@markdown Use these only if you want to push your trained model to a private repo in your Hugging Face Account
#@markdown If you dont use these, the model will be saved in Google Colab and you are required to download it manually.
#@markdown Please enter your Hugging Face write token. The trained model will be saved to your Hugging Face account.
#@markdown You can find your token here: https://huggingface.co./settings/tokens
push_to_hub = True # @param ["False", "True"] {type:"raw"}
hf_token = "hf_****" #@param {type:"string"}


#@markdown ---
#@markdown #### Hyperparameters
learning_rate = 3e-4 # @param {type:"number"}
num_epochs = 1 #@param {type:"number"}
batch_size = 1 # @param {type:"slider", min:1, max:32, step:1}
block_size = 1024 # @param {type:"number"}
trainer = "sft" # @param ["default", "sft"] {type:"raw"}
warmup_ratio = 0.1 # @param {type:"number"}
weight_decay = 0.01 # @param {type:"number"}
gradient_accumulation = 4 # @param {type:"number"}
mixed_precision = "fp16" # @param ["fp16", "bf16", "none"] {type:"raw"}
peft = True # @param ["False", "True"] {type:"raw"}
quantization = "int4" # @param ["int4", "int8", "none"] {type:"raw"}
lora_r = 16 #@param {type:"number"}
lora_alpha = 32 #@param {type:"number"}
lora_dropout = 0.05 #@param {type:"number"}

code for the creation of the dataset :
from datasets import load_dataset
dataset = load_dataset("camel-ai/math")#, streaming=True)

import pandas as pd
data = {"text":[]}

msg1 = dataset["train"]["message_1"]
msg2 = dataset["train"]["message_2"]

for i in range(3500, 7000):
    user = "<|user|>"+ msg1[i] +"<|end|>\n"
    phi = "<|assistant|>"+ msg2[i] +"<|end|>"
    prompt = user+phi
    data["text"].append(prompt)
    
data = pd.DataFrame.from_dict(data)
print(data)
#os.mkdir("/kaggle/working/data")
data.to_csv('data/dataset.csv', index=False, escapechar='\\')

!autotrain llm \
--train \
--username "styalai" \
--merge-adapter \
--model ${MODEL_NAME} \
--project-name ${PROJECT_NAME} \
--data-path data/ \
--text-column text \
--lr ${LEARNING_RATE} \
--batch-size ${BATCH_SIZE} \
--epochs ${NUM_EPOCHS} \
--block-size ${BLOCK_SIZE} \
--warmup-ratio ${WARMUP_RATIO} \
--lora-r ${LORA_R} \
--lora-alpha ${LORA_ALPHA} \
--lora-dropout ${LORA_DROPOUT} \
--weight-decay ${WEIGHT_DECAY} \
--gradient-accumulation ${GRADIENT_ACCUMULATION} \
--quantization ${QUANTIZATION} \
--mixed-precision ${MIXED_PRECISION} \
$( [[ "$PEFT" == "True" ]] && echo "--peft" ) \
$( [[ "$PUSH_TO_HUB" == "True" ]] && echo "--push-to-hub --token ${HF_TOKEN}" )q

durée de l’entrainement : 1:38:34