Instructions to use kyLELEng/retailops-instruct-qwen3.5-9b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kyLELEng/retailops-instruct-qwen3.5-9b with Transformers:

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

pipe = pipeline("text-generation", model="kyLELEng/retailops-instruct-qwen3.5-9b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kyLELEng/retailops-instruct-qwen3.5-9b")
model = AutoModelForCausalLM.from_pretrained("kyLELEng/retailops-instruct-qwen3.5-9b")
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

vLLM

How to use kyLELEng/retailops-instruct-qwen3.5-9b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kyLELEng/retailops-instruct-qwen3.5-9b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kyLELEng/retailops-instruct-qwen3.5-9b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kyLELEng/retailops-instruct-qwen3.5-9b

SGLang

How to use kyLELEng/retailops-instruct-qwen3.5-9b 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 "kyLELEng/retailops-instruct-qwen3.5-9b" \
    --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": "kyLELEng/retailops-instruct-qwen3.5-9b",
		"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 "kyLELEng/retailops-instruct-qwen3.5-9b" \
        --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": "kyLELEng/retailops-instruct-qwen3.5-9b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use kyLELEng/retailops-instruct-qwen3.5-9b with Docker Model Runner:
```
docker model run hf.co/kyLELEng/retailops-instruct-qwen3.5-9b
```

retailops-instruct-qwen3.5-9b / README.md

kyLELEng

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	---
	license: apache-2.0
	library_name: transformers
	base_model: Qwen/Qwen3.5-9B
	pipeline_tag: text-generation
	datasets:
	- kyLELEng/retailops-instruct-sft
	tags:
	- retail
	- ecommerce
	- instruction-tuning
	- product-listing
	- qwen
	- merged
	- text-generation
	- safetensors
	metrics:
	- loss
	- json_validity
	---

	# RetailOps-Instruct Qwen3.5-9B

	RetailOps-Instruct is a full merged Transformers checkpoint for e-commerce catalog operations.

	It is built by merging:

	- Base model: [`Qwen/Qwen3.5-9B`](https://huggingface.co/Qwen/Qwen3.5-9B)
	- LoRA adapter: [`kyLELEng/retailops-instruct-qwen3.5-9b-lora`](https://huggingface.co/kyLELEng/retailops-instruct-qwen3.5-9b-lora)

	No additional training is performed during this merge step.

	## Task

	The model is intended to turn product metadata, category information, seller notes, and customer reviews into structured listing packages:

	- `optimized_title`
	- `bullet_points`
	- `description`
	- `attributes`
	- `seo_keywords`
	- `faq`
	- `review_driven_improvements`
	- `compliance_notes`

	## Training Data

	- SFT dataset: [`kyLELEng/retailops-instruct-sft`](https://huggingface.co/datasets/kyLELEng/retailops-instruct-sft)
	- Sources in v1:
	- `AI4H/EC-Guide`
	- `McAuley-Lab/Amazon-Reviews-2023`
	- `McAuley-Lab/Amazon-C4`

	## Training Summary

	- Method: LoRA SFT, then merged into the base model
	- LoRA rank: 64
	- LoRA alpha: 128
	- LoRA dropout: 0.05
	- Max sequence length: 2048

	## Evaluation

	Final adapter eval before merge:

	```json
	{
	"json_validity_rate": 0.5,
	"avg_required_field_completion": 0.171875,
	"num_generation_eval_samples": 8
	}
	```

	No-thinking generation check:

	```json
	{
	"inference_mode": "tokenizer.apply_chat_template(..., enable_thinking=False) when supported",
	"num_generation_eval_samples": 24,
	"json_validity_rate": 1.0,
	"listing_num_samples": 13,
	"listing_json_validity_rate": 1.0,
	"listing_avg_required_field_completion": 0.7980769230769231
	}
	```

	## Recommended Inference

	Qwen3.5 models may emit reasoning text by default. For this RetailOps model, use no-thinking mode when available:

	```python
	prompt = tokenizer.apply_chat_template(
	messages,
	tokenize=False,
	add_generation_prompt=True,
	enable_thinking=False,
	)
	```

	## Example

	```python
	import json
	import torch
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_id = "kyLELEng/retailops-instruct-qwen3.5-9b"
	tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	torch_dtype=torch.bfloat16,
	device_map="auto",
	trust_remote_code=True,
	)

	messages = [
	{"role": "system", "content": "You are RetailOps-Instruct, an e-commerce catalog optimization assistant. Return exactly one valid JSON object. Do not invent unsupported product features. Use product metadata and reviews as the source of truth."},
	{"role": "user", "content": "Given the product metadata, category, and customer reviews, generate an optimized product listing package.\n\nINPUT:\n" + json.dumps({
	"category": "Home & Kitchen > Kitchen & Dining > Small Appliances",
	"brand": "ExampleBrand",
	"raw_title": "Portable Electric Kettle",
	"raw_description": "Small kettle for boiling water.",
	"product_specs": {"capacity": "1.0L", "material": "stainless steel", "safety": "auto shut-off"},
	"positive_reviews": ["Heats water quickly.", "Good size for small apartments."],
	"negative_reviews": ["The instructions were unclear.", "The outside gets warm after use."],
	"brand_voice": "clear, practical, trustworthy"
	}, ensure_ascii=False, indent=2)},
	]

	prompt = tokenizer.apply_chat_template(
	messages,
	tokenize=False,
	add_generation_prompt=True,
	enable_thinking=False,
	)
	inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
	with torch.no_grad():
	output = model.generate(**inputs, max_new_tokens=900, do_sample=False)
	print(tokenizer.decode(output[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
	```

	## Merge Smoke Test

	```json
	{
	"input": {
	"category": "Home & Kitchen > Kitchen & Dining > Small Appliances",
	"brand": "ExampleBrand",
	"raw_title": "Portable Electric Kettle",
	"raw_description": "Small kettle for boiling water.",
	"product_specs": {
	"capacity": "1.0L",
	"material": "stainless steel",
	"safety": "auto shut-off"
	},
	"positive_reviews": [
	"Heats water quickly.",
	"Good size for small apartments."
	],
	"negative_reviews": [
	"The instructions were unclear.",
	"The outside gets warm after use."
	],
	"brand_voice": "clear, practical, trustworthy"
	},
	"generated": "{\"optimized_title\": \"Portable Electric Kettle\", \"bullet_points\": [\"Small kettle for boiling water.\"], \"description\": \"Small kettle for boiling water.\", \"seo_keywords\": [\"kettle\", \"small\", \"portable\", \"electric\", \"boiling\", \"water\", \"examplebrand\", \"home\", \"kitchen\", \"dining\"], \"attributes\": {\"brand\": \"ExampleBrand\", \"category\": \"Home & Kitchen > Kitchen & Dining > Small Appliances\", \"features\": [\"stainless steel\", \"auto shut-off\"], \"specifications\": {\"capacity\": \"1.0L\", \"material\": \"stainless steel\", \"safety\": \"auto shut-off\"}}, \"review_driven_improvements\": [\"Highlight review-supported strengths: water, heats, quickly, good, size\", \"Clarify or avoid overclaiming around reported issues: instructions, were, unclear, outside, gets\", \"Do not contradict the supplied customer review evidence.\"], \"faq\": [{\"question\": \"Who is the brand?\", \"answer\": \"ExampleBrand\"}, {\"question\": \"What category does this product fit?\", \"answer\": \"Home & Kitchen > Kitchen & Dining > Small Appliances\"}], \"compliance_notes\": [\"No unsupported product features added.\", \"No medical, safety, or durability claims added unless present in the input.\"]}"
	}
	```

	## Limitations

	- The model can still produce unsupported claims; downstream validation is recommended.
	- This is not a compliance certification system.
	- Outputs should be checked before production publishing.