Instructions to use yashi29/qa-engineer-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yashi29/qa-engineer-v1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="yashi29/qa-engineer-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yashi29/qa-engineer-v1")
model = AutoModelForCausalLM.from_pretrained("yashi29/qa-engineer-v1", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yashi29/qa-engineer-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yashi29/qa-engineer-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yashi29/qa-engineer-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/yashi29/qa-engineer-v1

SGLang

How to use yashi29/qa-engineer-v1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "yashi29/qa-engineer-v1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yashi29/qa-engineer-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "yashi29/qa-engineer-v1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yashi29/qa-engineer-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use yashi29/qa-engineer-v1 with Docker Model Runner:
```
docker model run hf.co/yashi29/qa-engineer-v1
```

qa-engineer-v1

QLoRA fine-tune of unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit, trained to write software test plans like a 6-7 year experienced QA engineer. This checkpoint is merged (LoRA folded into the base weights) -- loadable directly with transformers, no PEFT or Unsloth required at inference time. See requirements.txt in this repo for the minimal inference dependencies.

Intended use

Prompt the model with this fixed instruction followed by a software requirement:

You are a senior QA engineer with 6-7 years of professional testing experience. Given the following software requirement, produce a comprehensive, well-structured test plan. Your response MUST use exactly these Markdown section headings, in this order: ## Requirement Summary & Assumptions, ## Missing Requirement Suggestions, ## Risk Analysis, ## Functional Test Cases, ## Negative Test Cases, ## Boundary Value Test Cases, ## Edge Cases, ## Security Test Cases, ## API Test Cases, ## Performance Test Cases, ## Accessibility Test Cases, ## Cross-Browser / Device Test Cases, ## Test Data Suggestions. For the API Test Cases section, write 'Not applicable — no API surface described in this requirement.' if the requirement is purely UI-only. Each test case must be written as: **TC-<PREFIX><NN>: <Title>** followed by a short bulleted list with Preconditions, Steps, Expected Result, and Priority (High/Medium/Low). Use ID prefixes: F for functional, N for negative, B for boundary, E for edge, S for security, A for API, P for performance, AC for accessibility, X for cross-browser/device.

Output is a fixed 13-section Markdown test plan (Requirement Summary & Assumptions, Missing Requirement Suggestions, Risk Analysis, Functional/Negative/Boundary/Edge/Security/API/Performance/Accessibility/Cross-Browser Test Cases, Test Data Suggestions).

Training

Method: QLoRA via Unsloth, LoRA r=16, alpha=16, target_modules=[o_proj, k_proj, q_proj, up_proj, gate_proj, v_proj, down_proj]
Dataset: data/versions/v1/train.jsonl (60 examples)
Epochs: 3.0
Final training loss: 1.490
Trained fresh from the base model (not continual/chained across versions).
Never trained on the held-out benchmark set used for evaluation below.

Evaluation

Scored on 25 held-out benchmark items, blending rule-based checks with a local LLM judge (8 rubric categories, 0-10 each).

Category	Score (0-10)
Functional Coverage	7.79
Edge Case Coverage	4.23
Security Coverage	3.85
Negative Test Coverage	6.08
Requirement Understanding	9.08
Missing Requirement Detection	7.16
Output Consistency	1.15
Formatting Consistency	5.71
Overall	5.63 (56.3%)

Hallucination rate: 0.48
Missed edge case count: 22/25
Missed security case count: 23/25

License

Inherits the base model's license -- check unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit before any commercial use.

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Safetensors

Model size

3B params

Tensor type

BF16