alexxhchen/qwen2-7b-consulting-interviewer-lora

Qwen2-7B Consulting Interviewer LoRA

A LoRA adapter fine-tuned for bilingual consulting interview and market-research conversations. It is designed to evaluate candidate responses, ask structured follow-up questions, and guide case-style business analysis.

This repository contains a PEFT LoRA adapter only. It is not a standalone base model.

Model Details

• —Developed by: Xiaohan Chen
• —Model type: LoRA adapter for causal language modeling
• —Base model: unsloth/Qwen2-7B-Instruct-bnb-4bit
• —Original model family: Qwen2-7B-Instruct
• —Languages: Simplified Chinese, English
• —License: Apache License 2.0
• —Framework: PEFT
• —Primary task: Text generation
• —Intended domain: Consulting interviews, market research, case interviews, and structured business analysis

Intended Use

Direct Use

Use this adapter with the specified Qwen2 base model to:

• —Conduct consulting-style mock interviews.
• —Evaluate candidate answers.
• —Ask structured follow-up questions.
• —Explore market entry, pricing, growth, customer segmentation, competition, channels, operations, and implementation topics.
• —Generate bilingual Chinese and English consulting-interview dialogue.

Example Behavior

Given a candidate response, the model should provide a concise, professional reply that may include:

1. 1.Feedback on the answer structure.
2. 2.Missing analytical dimensions.
3. 3.A follow-up question.
4. 4.Suggested consulting frameworks.
5. 5.Relevant commercial risks or implementation considerations.

Out-of-Scope Use

This model is not designed for:

• —Legal, medical, financial, or other high-stakes professional advice.
• —Factual research requiring verified current information.
• —Employment decisions without qualified human review.
• —Replacing a human interviewer, consultant, researcher, or subject-matter expert.
• —Generating confidential client information or proprietary analysis.

Limitations

• —This adapter was trained on a relatively small domain-specific dataset and may overuse common consulting frameworks.
• —Outputs can contain inaccurate, incomplete, generic, or fabricated information.
• —The model does not have guaranteed access to current market data, regulations, company information, or news.
• —Chinese and English output quality may vary by topic, prompt wording, and conversation length.
• —The model may reproduce biases, assumptions, or limitations present in its training data or base model.
• —It should not be used as the sole basis for business, hiring, investment, or strategy decisions.

Recommendations

• —Review all outputs before using them in client-facing, hiring, or business contexts.
• —Provide concrete market, company, customer, and case context in prompts.
• —Use retrieval, verified data sources, or human review for factual claims.
• —Test the adapter on representative interview scenarios before production deployment.
• —Keep the base model version fixed during evaluation and deployment.

Training Data

The adapter was trained on approximately 1,460 curated consulting-interview and market-research examples.

The dataset contains instruction, input, and output fields. Examples were formatted as consulting interviewer interactions, where the input represents a candidate response or interview context and the output represents a professional interviewer response or follow-up question.

The training dataset is not included in this repository.

Training Procedure

Prompt Format

Training examples used an Alpaca-style instruction format:

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:
{instruction}

### Input:
{input}

### Response:
{output}
LoRA Configuration
- LoRA rank (r): 16
- LoRA alpha: 16
- LoRA dropout: 0
- Bias: none
- Target modules:
- q_proj
- k_proj
- v_proj
- o_proj
- gate_proj
- up_proj
- down_proj
Training Hyperparameters
- Maximum sequence length: 1024
- Training steps: 300
- Per-device batch size: 4
- Gradient accumulation steps: 4
- Effective batch size: 16
- Learning rate: 2e-4
- Warmup steps: 5
- Optimizer: adamw_8bit
- Weight decay: 0.01
- Learning-rate scheduler: linear
- Random seed: 3407
- Precision: FP16 on NVIDIA T4 hardware
- Training method: Supervised fine-tuning with TRL SFTTrainer
Evaluation
No formal benchmark evaluation has been completed.
Informal qualitative testing showed that the adapter can generate structured Chinese consulting-interview responses for market-entry and business-case prompts. This does not establish factual accuracy, robustness, safety, or production readiness.
How to Use
Install the required libraries:
pip install transformers peft accelerate bitsandbytes torch
Load the base model and adapter:
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model_id = "unsloth/Qwen2-7B-Instruct-bnb-4bit"
adapter_id = "alexxhchen/qwen2-7b-consulting-interviewer-lora"

tokenizer = AutoTokenizer.from_pretrained(adapter_id)

base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    device_map="auto",
    load_in_4bit=True,
)

model = PeftModel.from_pretrained(base_model, adapter_id)
model.eval()
Example prompt:
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:
作为一名专业的咨询公司面试官，请根据候选人的回答进行专业的回复与追问。

### Input:
我会先分析市场规模、目标客户和竞争格局，再决定是否进入该市场。

### Response:
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    do_sample=True,
    temperature=0.4,
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Compute Infrastructure
- Training platform: Google Colab
- GPU: NVIDIA Tesla T4
- GPU memory: approximately 14.5 GB
- Approximate training duration: 72 minutes
- Inference optimization: Unsloth
Framework Versions
- PEFT: 0.19.1
- Transformers: 5.5.0
- TRL: 0.24.0
- Unsloth: 2026.8.1
- PyTorch: 2.11.0
- CUDA: 12.8
License
This LoRA adapter is released under the Apache License 2.0.
Use of this adapter also requires compliance with the license and terms of the underlying Qwen2 base model and any deployment platform or inference provider terms.
Citation
If you use this adapter, please cite:
@software{xiaochen2026qwen2consultinginterviewer,
  author = {Xiaohan Chen},
  title = {Qwen2-7B Consulting Interviewer LoRA},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {Hugging Face Model Repository}
}
Contact
- Author: Xiaohan Chen
- Repository: alexxhchen/qwen2-7b-consulting-interviewer-lora