metadata
license: mit
base_model: unsloth/Qwen2.5-3B-Instruct
library_name: peft
tags:
- qwen2.5
- unsloth
- lora
- peft
- interview
- software-engineering
- text-generation
language:
- en
pipeline_tag: text-generation
datasets:
- shimogerald/interview-coach-dataset
Interview Coach LoRA (Qwen2.5-3B-Instruct)
LoRA adapter fine-tuned for software-engineering interview Q&A coaching.
Model Details
- Base model:
unsloth/Qwen2.5-3B-Instruct - Method: QLoRA (4-bit) + LoRA via Unsloth
- LoRA:
r=16,lora_alpha=16,lora_dropout=0 - Target modules:
q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj - Context length: 2048
- Language: English
Training Data
Fine-tuned on shimogerald/interview-coach-dataset (chat messages format, ~90/10 train/val).
Intended Use
Practice / coaching-style answers to technical interview questions (APIs, systems, coding concepts, behavioral, etc.).
Limitations
- Synthetic training data may contain errors
- Not a substitute for real interview feedback
- May hallucinate technical details
- English only
How to Use
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="shimogerald/lora_interview_coach",
max_seq_length=2048,
load_in_4bit=True,
)
FastLanguageModel.for_inference(model)
messages = [{"role": "user", "content": "What is the difference between PUT and PATCH?"}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=256, do_sample=False)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
If loading the adapter separately fails, load the base model then attach this repo with PEFT PeftModel.from_pretrained.
Training Setup (summary)
- Optimizer: AdamW
- LR schedule: cosine with warmup
- Epochs: 3
- Framework: Unsloth + Accelerate + Transformers
This qwen2 model was trained 2x faster with Unsloth
