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Model: owlgebra-ai/wufus-CART-8B Source: Original Platform
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---
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language:
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- en
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license: apache-2.0
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base_model: Qwen/Qwen3-8B
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tags:
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- reinforcement-learning
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- tool-calling
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- e-commerce
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- shopping-assistant
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- GRPO
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- DAPO
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- multi-turn
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pipeline_tag: text-generation
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library_name: transformers
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---
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# WUFUS(CART) — E-Commerce Shopping Cart Assistant
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**wufus-CART-8B** is a fine-tuned [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) model, created via on-policy DAPO training(RL) using OpenEnv, specialized for multi-turn, tool-augmented e-commerce shopping conversations. The model helps customers discover products, compare variants, analyse user history and build accurate shopping carts through natural dialogue.
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## Key Capabilities
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- **Product Discovery**: Searches a product catalog using formulated queries
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- **Variant Selection**: Identifies correct color, size, and other variant attributes
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- **Cart Management**: Adds products with correct quantities and variants
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- **Clarification Dialogue**: Asks follow-up questions when customer requests are ambiguous
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- **Multi-Item Orders**: Handles requests for multiple different products in one conversation
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## Quick Start
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model = AutoModelForCausalLM.from_pretrained(
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"owlgebra-ai/wufus-CART-8B",
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torch_dtype=torch.bfloat16,
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device_map="auto",
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trust_remote_code=True,
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attn_implementation="flash_attention_2", # optional, for speed
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)
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tokenizer = AutoTokenizer.from_pretrained("owlgebra-ai/wufus-CART-8B", trust_remote_code=True)
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```
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## System Prompt
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Use the following system prompt for optimal performance:
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```
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You are a shopping cart assistant. Help customers add the correct products to their cart.
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WORKFLOW:
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Step 0 (COUNT): Count how many distinct items the customer wants. Plan one search per item.
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Step 1 (GATHER): Call user_get_visit_history. Then call catalog_search ONCE PER ITEM with a focused query.
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Step 2 (IDENTIFY): Match each item to a specific product_id from search results.
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Step 3 (CLARIFY): If color/size/quantity is missing, call ask_user to get the details.
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Step 4 (VARIANTS): Call catalog_get_variants for each product to find the right variant.
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Step 5 (ADD): Call cart_add for each item with the correct product_id, variant_id, and quantity.
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Step 6 (VERIFY): Call cart_view. Compare cart contents against the original request item-by-item.
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MULTI-ITEM EXAMPLE (2 items):
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User: "Add a blue phone case and 3 screen protectors"
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→ catalog_search("blue phone case")
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→ catalog_search("screen protectors")
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→ catalog_get_variants(phone_case_id)
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→ cart_add(phone_case_id, blue_variant, qty=1)
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→ cart_add(protector_id, variant, qty=3)
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→ cart_view()
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```
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## Tool Definitions
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The model is trained to use the following tools via native Qwen3 tool-calling format:
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### `catalog_search`
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Search the product catalog for products matching a text query.
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```json
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{
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"name": "catalog_search",
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"parameters": {
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": "Natural language description of the desired product."
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}
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},
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"required": ["query"]
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}
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}
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```
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**Returns**: List of product dicts with `product_id`, `title`, `price`, `rating`, `stock_qty`, `key_attrs`.
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### `catalog_get_variants`
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Get available variants (color, size, etc.) for a specific product.
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```json
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{
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"name": "catalog_get_variants",
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"parameters": {
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"type": "object",
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"properties": {
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"product_id": {
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"type": "string",
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"description": "The product ID to retrieve variants for."
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}
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},
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"required": ["product_id"]
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}
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}
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```
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**Returns**: List of variant dicts with `variant_id`, `attrs` (e.g. color, size), `price_delta`, `stock_qty`.
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### `cart_add`
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Add a product to the shopping cart.
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```json
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{
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"name": "cart_add",
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"parameters": {
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"type": "object",
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"properties": {
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"product_id": {
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"type": "string",
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"description": "The product ID to add (from catalog_search results)."
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},
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"variant_id": {
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"type": "string",
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"description": "Optional variant ID for specific color/size selection."
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},
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"quantity": {
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"type": "integer",
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"description": "Number of units to add. Defaults to 1."
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}
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},
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"required": ["product_id"]
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}
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}
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```
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**Returns**: Updated cart summary with `lines` (list of items), `total_items`, `total_price`.
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### `cart_view`
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View the current contents of the shopping cart.
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```json
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{
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"name": "cart_view",
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"parameters": {
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"type": "object",
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"properties": {}
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}
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}
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```
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**Returns**: Cart summary with `lines`, `total_items`, `total_price`.
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### `user_get_visit_history`
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Get the customer's recently viewed products (browsing history).
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```json
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{
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"name": "user_get_visit_history",
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"parameters": {
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"type": "object",
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"properties": {}
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}
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}
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```
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**Returns**: List of recently viewed product cards with `product_id`, `title`, `price`, `category`, `brand`.
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### `ask_user`
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Ask the customer a clarification question about their order.
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```json
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{
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"name": "ask_user",
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"parameters": {
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"type": "object",
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"properties": {
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"question": {
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"type": "string",
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"description": "Your question to the customer, e.g. 'What color would you like?' or 'How many do you need?'"
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}
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},
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"required": ["question"]
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}
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}
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```
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**Returns**: The customer's response with the requested information.
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## Usage with Tool Calling
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```python
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tools = [
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{"type": "function", "function": {"name": "catalog_search", "description": "Search products", "parameters": {"type": "object", "properties": {"query": {"type": "string"}}, "required": ["query"]}}},
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{"type": "function", "function": {"name": "catalog_get_variants", "description": "Get variants for a product", "parameters": {"type": "object", "properties": {"product_id": {"type": "string"}}, "required": ["product_id"]}}},
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{"type": "function", "function": {"name": "cart_add", "description": "Add to cart", "parameters": {"type": "object", "properties": {"product_id": {"type": "string"}, "variant_id": {"type": "string"}, "quantity": {"type": "integer"}}, "required": ["product_id"]}}},
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{"type": "function", "function": {"name": "cart_view", "description": "View cart", "parameters": {"type": "object", "properties": {}}}},
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{"type": "function", "function": {"name": "user_get_visit_history", "description": "Get browsing history", "parameters": {"type": "object", "properties": {}}}},
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{"type": "function", "function": {"name": "ask_user", "description": "Ask customer a question", "parameters": {"type": "object", "properties": {"question": {"type": "string"}}, "required": ["question"]}}},
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]
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messages = [
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": "I need a pair of running shoes and 3 water bottles"},
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]
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text = tokenizer.apply_chat_template(messages, tools=tools, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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with torch.no_grad():
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outputs = model.generate(**inputs, max_new_tokens=2048, temperature=0.7, do_sample=True)
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response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False)
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print(response)
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```
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The model will produce tool calls in Qwen3's native format:
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```
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<tool_call>
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{"name": "catalog_search", "arguments": {"query": "running shoes"}}
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</tool_call>
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<tool_call>
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{"name": "catalog_search", "arguments": {"query": "water bottles"}}
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</tool_call>
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```
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Feed tool results back as `tool` role messages and continue the loop until the model produces a text-only response (conversation complete).
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## Training Details
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| Attribute | Value |
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|-----------|-------|
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| Base Model | [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) |
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| Parameters | 8.2B |
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| Precision | bf16 |
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| Context Length | 8192 tokens (tool-calling conversations) |
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| Training Method | GRPO/DAPO (Reinforcement Learning) |
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| Training Framework | TRL + vLLM + FSDP2 |
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| Tool Calling Format | Qwen3 native (`<tool_call>` / `</tool_call>`) |
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| Thinking Mode | Supported (Qwen3 `<think>` tokens) |
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## Intended Use
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This model is designed for integration into e-commerce platforms as a shopping cart assistant. It works best when:
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- Connected to a real product catalog via the tool interface
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- The catalog supports text search (e.g., FAISS, Elasticsearch)
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- Products have variant information (color, size, etc.)
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- The conversation is multi-turn with tool execution between turns
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## Limitations
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- Requires external tool implementations — the model generates tool calls but does not execute them
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- Trained on English product data only
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- Variant matching depends on catalog quality — ambiguous product names may cause errors
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## Citation
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If you use this model, please cite:
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```bibtex
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@software{ecomrlve2026,
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title={EcomRLVE-GYM: Reinforcement Learning with Adaptive Verifiable Environments for E-Commerce},
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year={2026},
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url={https://github.com/owlgebra-ai/EcomRLVE-Gym}
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}
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```
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0].role == 'system' %}
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{{- messages[0].content + '\n\n' }}
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{%- endif %}
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{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0].role == 'system' %}
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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{%- for message in messages[::-1] %}
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{%- set index = (messages|length - 1) - loop.index0 %}
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{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
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{%- set ns.multi_step_tool = false %}
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{%- set ns.last_query_index = index %}
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{%- endif %}
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{%- endfor %}
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{%- for message in messages %}
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{%- if message.content is string %}
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{%- set content = message.content %}
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{%- else %}
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{%- set content = '' %}
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{%- endif %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{%- set reasoning_content = '' %}
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{%- if message.reasoning_content is string %}
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{%- set reasoning_content = message.reasoning_content %}
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{%- else %}
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{%- if '</think>' in content %}
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{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
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{%- set content = content.split('</think>')[-1].lstrip('\n') %}
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{%- endif %}
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{%- endif %}
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{%- if loop.index0 > ns.last_query_index %}
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{%- if loop.last or (not loop.last and reasoning_content) %}
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{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- if message.tool_calls %}
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{%- for tool_call in message.tool_calls %}
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{%- if (loop.first and content) or (not loop.first) %}
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{{- '\n' }}
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{%- endif %}
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{%- if tool_call.function %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{%- if tool_call.arguments is string %}
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{{- tool_call.arguments }}
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{%- else %}
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{{- tool_call.arguments | tojson }}
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{%- endif %}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{%- endif %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- if enable_thinking is defined and enable_thinking is false %}
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{{- '<think>\n\n</think>\n\n' }}
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{%- endif %}
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{%- endif %}
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71
config.json
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config.json
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{
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"dtype": "bfloat16",
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"eos_token_id": 151645,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 12288,
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"layer_types": [
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"full_attention",
|
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"full_attention",
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"full_attention",
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
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"full_attention",
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"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention"
|
||||
],
|
||||
"max_position_embeddings": 40960,
|
||||
"max_window_layers": 36,
|
||||
"model_type": "qwen3",
|
||||
"num_attention_heads": 32,
|
||||
"num_hidden_layers": 36,
|
||||
"num_key_value_heads": 8,
|
||||
"pad_token_id": null,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_parameters": {
|
||||
"rope_theta": 1000000,
|
||||
"rope_type": "default"
|
||||
},
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": false,
|
||||
"transformers_version": "5.5.3",
|
||||
"use_cache": true,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 151936
|
||||
}
|
||||
13
generation_config.json
Normal file
13
generation_config.json
Normal file
@@ -0,0 +1,13 @@
|
||||
{
|
||||
"bos_token_id": 151643,
|
||||
"do_sample": true,
|
||||
"eos_token_id": [
|
||||
151645,
|
||||
151643
|
||||
],
|
||||
"pad_token_id": 151643,
|
||||
"temperature": 0.6,
|
||||
"top_k": 20,
|
||||
"top_p": 0.95,
|
||||
"transformers_version": "5.5.3"
|
||||
}
|
||||
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:b3b0d00e9716ccbd0f07a6f1498592d1f89fd8c1e46b14cd1fb6b9d2e0c8de42
|
||||
size 16381517208
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
|
||||
size 11422650
|
||||
29
tokenizer_config.json
Normal file
29
tokenizer_config.json
Normal file
@@ -0,0 +1,29 @@
|
||||
{
|
||||
"add_prefix_space": false,
|
||||
"backend": "tokenizers",
|
||||
"bos_token": null,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"is_local": false,
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
Reference in New Issue
Block a user