LlamaFactoryAI

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cv-job-description-matching

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Llama Factory 8

change C:\Users\XXX.cache\huggingface\hub\models--LlamaFactoryAI--cv-job-description-matching\snapshots\b5ca72308137610a605d45e4bcd887550363b783\adapter_config.json
"task_type": "QUESTION_ANS",

and try this

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel, PeftConfig
import os
base_model_name = "akjindal53244/Llama-3.1-Storm-8B"
os.makedirs("./offload", exist_ok=True)
device = "cuda" if torch.cuda.is_available() else "cpu"
# Load the base model
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    load_in_8bit=False,
    load_in_4bit=False,
    offload_folder="./offload",
)
tokenizer = AutoTokenizer.from_pretrained(
    base_model_name,
)
if tokenizer.pad_token is None:
    tokenizer.pad_toke = "[PAD]"
    tokenizer.add_special_tokens({"pad_token": "[PAD]"})
    base_model.resize_token_embeddings(len(tokenizer))
# Load the LoRA adapter
peft_model_id = "LlamaFactoryAI/cv-job-description-matching"
config = PeftConfig.from_pretrained(peft_model_id)
model = PeftModel.from_pretrained(
    base_model,
    peft_model_id,
    torch_dtype=torch.bfloat16,
    load_in_8bit=False,
    load_in_4bit=False,
    offload_folder="./offload",
).to(device)

# Use the model
messages = [
    {
        "role": "system",
        "content": """You are an advanced AI model designed to analyze the compatibility between a CV and a job description. You will receive a CV and a job description. Your task is to output a structured JSON format that includes the following:

1. matching_analysis: Analyze the CV against the job description to identify key strengths and gaps.
2. description: Summarize the relevance of the CV to the job description in a few concise sentences.
3. score: Provide a numerical compatibility score (0-100) based on qualifications, skills, and experience.
4. recommendation: Suggest actions for the candidate to improve their match or readiness for the role.

Your output must be in JSON format as follows:
{
  "matching_analysis": "Your detailed analysis here.",
  "description": "A brief summary here.",
  "score": 85,
  "recommendation": "Your suggestions here."
}
""",
    },
    {
        "role": "user",
        "content": "<CV> {cv} </CV>\n<job_description> {job_description} </job_description>",
    },
]

cv = """
???
"""  # Replace with actual CV
job_description = """
???
""" 
# Replace with actual job description
messages[1]["content"] = messages[1]["content"].format(
    cv=cv, job_description=job_description
)

inputs = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, return_tensors="pt", return_dict=True
).to(device)


outputs = model.generate(
        input_ids=inputs["input_ids"],
        attention_mask=inputs["attention_mask"],
        max_new_tokens=128,
        do_sample=True,
        temperature=0.7,
        top_p=0.9,
        pad_token_id=tokenizer.pad_token_id,
        eos_token_id=tokenizer.eos_token_id
    )
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Okay. I will try to do this
Thank you.