metadata
base_model: unsloth/Qwen3-14B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
I edited my README.md locally, but unsloth hijacked it. That's not good.
Fine-tuned LoRA adapter from unsloth/Qwen3-14B-unsloth-bnb-4bit using unsloth.
Based on this tutorial.
Data
Training data is 237 scenarios for dependents eligible under 26 U.S.C. 152 (a)-(d), generated with gemini-2.5-pro-preview-03-25, but not checked for correctness.
Training arguments on A100 (40GB):
TrainingArguments(
per_device_train_batch_size=8,
gradient_accumulation_steps=4,
num_train_epochs=16,
warmup_steps=16,
learning_rate=2e-4,
fp16=not is_bfloat16_supported(),
bf16=is_bfloat16_supported(),
logging_steps=10,
optim="adamw_8bit",
weight_decay=0.01,
lr_scheduler_type="linear",
seed=3407, # https://arxiv.org/abs/2109.08203
output_dir="outputs",
report_to="none",
)
Usage
To use:
from unsloth import FastLanguageModel
max_seq_length = 2048
dtype = None
load_in_4bit = True
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="doabell/dependent-qlora", # YOUR MODEL YOU USED FOR TRAINING
max_seq_length=max_seq_length,
dtype=dtype,
load_in_4bit=load_in_4bit,
)
FastLanguageModel.for_inference(model)
Template:
alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
You are an experienced lawyer in dealing with dependents for US tax purposes.
### Input:
{}
### Response:
{}"""
Streaming:
from transformers import TextStreamer
FastLanguageModel.for_inference(model)
inputs = tokenizer(
[
alpaca_prompt.format(
"Can I claim my 7 year old son? He is an instagram influencer and earned $505 last year.",
"",
)
],
return_tensors="pt",
).to("cuda")
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer=text_streamer, max_new_tokens=256)
No streaming:
FastLanguageModel.for_inference(model)
inputs = tokenizer(
[
alpaca_prompt.format(
"Can I claim my 7 year old son? He is an instagram influencer and earned $5050 last year.",
"",
)
],
return_tensors="pt",
).to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256, use_cache=True)
tokenizer.batch_decode(outputs)