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1training_args = TrainingArguments(
2 per_device_train_batch_size=2,
3 gradient_accumulation_steps=4,
4 warmup_steps=5,
5 max_steps=60,
6 learning_rate=2e-4,
7 fp16=not is_bfloat16_supported(),
8 bf16=is_bfloat16_supported(),
9 optim="adamw_8bit",
10 weight_decay=0.01,
11 lr_scheduler_type="linear",
12 seed=3407,
13)
14
15# LoRA Configuration
16lora_config = {
17 "r": 16,
18 "target_modules": ["q_proj", "k_proj", "v_proj", "o_proj",
19 "gate_proj", "up_proj", "down_proj"],
20 "lora_alpha": 16,
21 "lora_dropout": 0,
22 "bias": "none",
23 "use_gradient_checkpointing": "unsloth"
24}Below is a job application context. Write a professional cover letter based on the provided information.
### Job Details:
Title: {job_title}
Preferred Qualifications: {preferred_quals}
Company: {company}
### Applicant Information:
Name: {applicant_name}
Past Experience: {past_exp}
Current Experience: {current_exp}
Skills: {skills}
Qualifications: {qualifications}
### Cover Letter:1# Create Modelfile
2echo "FROM llama3.2:3b
3ADAPTER /path/to/downloaded/lora/weights" > Modelfile
4
5# Create custom model
6ollama create coverletter-custom -f Modelfile1@misc{cover-letter-llama,
2 author = {Atharva},
3 title = {Cover Letter LLaMA 3.2 (LoRA-tuned)},
4 year = {2024},
5 publisher = {Hugging Face},
6 journal = {Hugging Face Model Hub},
7 howpublished = {\url{https://huggingface.co/Atharva2099/cover-letter-llama-3.2-lora}}
8}