Fine-Tuning DeepSeek R1 for Resume Analysis: A Step-by-Step Guide

Fine-tuning DeepSeek R1 for resume analysis involves several steps that help customize the model to better understand and process resume data. Here's a detailed guide on how to achieve this:

1. Environment Setup**

To start fine-tuning DeepSeek R1, you need to set up your environment. This typically involves using a cloud IDE like Kaggle, which provides access to powerful GPUs necessary for training large language models. You will also need to install necessary libraries and set up your Hugging Face and Weights & Biases (WandB) tokens as secrets in your environment[1].

python
# Install necessary libraries
!pip install unsloth
!pip install --force-reinstall --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth.git

# Log in to Hugging Face and WandB
from huggingface_hub import login
from kaggle_secrets import UserSecretsClient

user_secrets = UserSecretsClient()
hf_token = user_secrets.get_secret("HUGGINGFACE_TOKEN")
login(hf_token)

import wandb
wb_token = user_secrets.get_secret("wandb")
wandb.login(key=wb_token)

2. Loading the Model and Tokenizer**

Load the DeepSeek R1 model and its tokenizer. For efficiency, consider using a distilled version of the model, such as DeepSeek-R1-Distill-Llama-8B, which is more manageable on consumer hardware or cloud services like Kaggle[4].

python
from unsloth import FastLanguageModel

max_seq_length = 2048
dtype = None
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="unsloth/DeepSeek-R1-Distill-Llama-8B",
    max_seq_length=max_seq_length,
    dtype=dtype,
    load_in_4bit=load_in_4bit,
    token=hf_token,
)

3. Preprocessing the Dataset**

Prepare your resume dataset by converting resumes into a format that the model can process. This might involve extracting relevant sections like experience, education, and skills, and formatting them into prompts that guide the model's analysis[7].

4. Fine-Tuning the Model**

Apply fine-tuning techniques such as using LoRA adapters for efficient fine-tuning. LoRA adapters allow you to modify the model's weights minimally while still achieving significant performance improvements on your specific task[2].

python
# Example of applying LoRA adapters
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel, PeftConfig

# Load the model and tokenizer
model_name = "unsloth/DeepSeek-R1-Distill-Llama-8B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Define the LoRA adapter configuration
lora_config = PeftConfig.from_pretrained(
    "lora-base",
    adapter_name="lora",
    config_path=None,
    state_dict=None,
    device_map=None,
    dtype=None,
    train_lora_only=True,
)

# Create the LoRA model
lora_model = PeftModel.from_pretrained(
    model,
    lora_config=lora_config,
    adapter_name="lora",
    device_map=None,
    dtype=None,
)

# Train the LoRA model on your dataset
# This involves creating a custom dataset class and a training loop
# to update the LoRA adapter weights.

5. Model Inference and Evaluation**

After fine-tuning, use your model to analyze resumes by crafting prompts that guide the model to provide detailed feedback. Evaluate the model's performance on a test set to ensure it meets your requirements[7].

python
# Example of crafting a prompt for resume analysis
def _construct_prompt(resume_content: str) -> str:
    base_prompt = f"""Please analyze this resume and provide detailed feedback in the following format:

Resume Content:

{resume_content}

Please provide your analysis in JSON format with the following structure:

{
"summary": "Brief overview of the resume",
"strengths": ["List of key strengths"],
"weaknesses": ["Areas for improvement"],
"recommendations": ["Specific recommendations"],
"keywords_missing": ["Important keywords that should be added"],
"formatting_suggestions": ["Suggestions for better formatting"],
"impact_score": "Score out of 10",
"detailed_feedback": {
"experience": "Feedback on experience section",
"education": "Feedback on education section",
"skills": "Feedback on skills section"
}
}
"""
    return base_prompt

# Use the model to generate feedback
inputs = tokenizer([_construct_prompt("Your Resume Content")], return_tensors="pt").to("cuda")
outputs = model.generate(
    input_ids=inputs.input_ids,
    attention_mask=inputs.attention_mask,
    max_new_tokens=1200,
    use_cache=True,
)
response = tokenizer.batch_decode(outputs)
print(response)

6. Prompt Optimization**

Continuously refine your prompts based on user feedback to enhance output accuracy and relevance. Experimenting with phrasing and structure can guide the model to deliver more actionable insights[7].

7. Future Enhancements**

Consider expanding your tool's capabilities by adding multilingual support or developing industry-specific templates for feedback formats. This could enhance the tool's value for global companies or specific sectors like healthcare or IT[7].

Citations:
[1] https://www.datacamp.com/tutorial/fine-tuning-deepseek-r1-reasoning-model
[2] https://www.kdnuggets.com/how-to-fine-tune-deepseek-r1-custom-dataset
[3] https://www.linkedin.com/posts/glencathey_check-out-how-deepseeks-r1-transparently-activity-7290398540256727040-HQaW
[4] https://www.firecrawl.dev/blog/fine-tuning-deepseek
[5] https://www.nscale.com/blog/distributed-fine-tuning-at-scale-deepseek-r1
[6] https://www.reddit.com/r/LocalLLaMA/comments/1i8v54i/how_can_i_finetune_deepseekr1/
[7] https://blog.stackademic.com/integrating-deepseek-r1-with-fastapi-building-an-ai-powered-resume-analyzer-code-demo-4e1cc29cdc6e
[8] https://techcommunity.microsoft.com/blog/machinelearningblog/fine-tuning-deepseek-r1-distill-llama-8b-with-pytorch-fsdp-qlora-on-azure-machin/4377965

How can I fine-tune DeepSeek R1 for resume analysis