---
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`](https://huggingface.co/datasets/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

```python
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](https://github.com/unslothai/unsloth)

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)