wufus-CART-8B is a fine-tuned 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.

Key Capabilities

Product Discovery: Searches a product catalog using formulated queries
Variant Selection: Identifies correct color, size, and other variant attributes
Cart Management: Adds products with correct quantities and variants
Clarification Dialogue: Asks follow-up questions when customer requests are ambiguous
Multi-Item Orders: Handles requests for multiple different products in one conversation

Quick Start

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "owlgebra-ai/wufus-CART-8B",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
    attn_implementation="flash_attention_2",  # optional, for speed
)
tokenizer = AutoTokenizer.from_pretrained("owlgebra-ai/wufus-CART-8B", trust_remote_code=True)

System Prompt

Use the following system prompt for optimal performance:

You are a shopping cart assistant. Help customers add the correct products to their cart.

WORKFLOW:
Step 0 (COUNT): Count how many distinct items the customer wants. Plan one search per item.
Step 1 (GATHER): Call user_get_visit_history. Then call catalog_search ONCE PER ITEM with a focused query.
Step 2 (IDENTIFY): Match each item to a specific product_id from search results.
Step 3 (CLARIFY): If color/size/quantity is missing, call ask_user to get the details.
Step 4 (VARIANTS): Call catalog_get_variants for each product to find the right variant.
Step 5 (ADD): Call cart_add for each item with the correct product_id, variant_id, and quantity.
Step 6 (VERIFY): Call cart_view. Compare cart contents against the original request item-by-item.

MULTI-ITEM EXAMPLE (2 items):
  User: "Add a blue phone case and 3 screen protectors"
  → catalog_search("blue phone case")
  → catalog_search("screen protectors")
  → catalog_get_variants(phone_case_id)
  → cart_add(phone_case_id, blue_variant, qty=1)
  → cart_add(protector_id, variant, qty=3)
  → cart_view()

Tool Definitions

The model is trained to use the following tools via native Qwen3 tool-calling format:

`catalog_search`

Search the product catalog for products matching a text query.

json

{
  "name": "catalog_search",
  "parameters": {
    "type": "object",
    "properties": {
      "query": {
        "type": "string",
        "description": "Natural language description of the desired product."
      }
    },
    "required": ["query"]
  }
}

Returns: List of product dicts with product_id, title, price, rating, stock_qty, key_attrs.

`catalog_get_variants`

Get available variants (color, size, etc.) for a specific product.

json

{
  "name": "catalog_get_variants",
  "parameters": {
    "type": "object",
    "properties": {
      "product_id": {
        "type": "string",
        "description": "The product ID to retrieve variants for."
      }
    },
    "required": ["product_id"]
  }
}

Returns: List of variant dicts with variant_id, attrs (e.g. color, size), price_delta, stock_qty.

`cart_add`

Add a product to the shopping cart.

json

{
  "name": "cart_add",
  "parameters": {
    "type": "object",
    "properties": {
      "product_id": {
        "type": "string",
        "description": "The product ID to add (from catalog_search results)."
      },
      "variant_id": {
        "type": "string",
        "description": "Optional variant ID for specific color/size selection."
      },
      "quantity": {
        "type": "integer",
        "description": "Number of units to add. Defaults to 1."
      }
    },
    "required": ["product_id"]
  }
}

Returns: Updated cart summary with lines (list of items), total_items, total_price.

`cart_view`

View the current contents of the shopping cart.

json

{
  "name": "cart_view",
  "parameters": {
    "type": "object",
    "properties": {}
  }
}

Returns: Cart summary with lines, total_items, total_price.

`user_get_visit_history`

Get the customer's recently viewed products (browsing history).

json

{
  "name": "user_get_visit_history",
  "parameters": {
    "type": "object",
    "properties": {}
  }
}

Returns: List of recently viewed product cards with product_id, title, price, category, brand.

`ask_user`

Ask the customer a clarification question about their order.

json

{
  "name": "ask_user",
  "parameters": {
    "type": "object",
    "properties": {
      "question": {
        "type": "string",
        "description": "Your question to the customer, e.g. 'What color would you like?' or 'How many do you need?'"
      }
    },
    "required": ["question"]
  }
}

Returns: The customer's response with the requested information.

Usage with Tool Calling

python

tools = [
    {"type": "function", "function": {"name": "catalog_search", "description": "Search products", "parameters": {"type": "object", "properties": {"query": {"type": "string"}}, "required": ["query"]}}},
    {"type": "function", "function": {"name": "catalog_get_variants", "description": "Get variants for a product", "parameters": {"type": "object", "properties": {"product_id": {"type": "string"}}, "required": ["product_id"]}}},
    {"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"]}}},
    {"type": "function", "function": {"name": "cart_view", "description": "View cart", "parameters": {"type": "object", "properties": {}}}},
    {"type": "function", "function": {"name": "user_get_visit_history", "description": "Get browsing history", "parameters": {"type": "object", "properties": {}}}},
    {"type": "function", "function": {"name": "ask_user", "description": "Ask customer a question", "parameters": {"type": "object", "properties": {"question": {"type": "string"}}, "required": ["question"]}}},
]

messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {"role": "user", "content": "I need a pair of running shoes and 3 water bottles"},
]

text = tokenizer.apply_chat_template(messages, tools=tools, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=2048, temperature=0.7, do_sample=True)
response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False)
print(response)

The model will produce tool calls in Qwen3's native format:

<tool_call>
{"name": "catalog_search", "arguments": {"query": "running shoes"}}
</tool_call>
<tool_call>
{"name": "catalog_search", "arguments": {"query": "water bottles"}}
</tool_call>

Feed tool results back as tool role messages and continue the loop until the model produces a text-only response (conversation complete).

Training Details

Attribute	Value
Base Model	Qwen/Qwen3-8B
Parameters	8.2B
Precision	bf16
Context Length	8192 tokens (tool-calling conversations)
Training Method	GRPO/DAPO (Reinforcement Learning)
Training Framework	TRL + vLLM + FSDP2
Tool Calling Format	Qwen3 native (`<tool_call>` / `</tool_call>`)
Thinking Mode	Supported (Qwen3 `<think>` tokens)

Intended Use

This model is designed for integration into e-commerce platforms as a shopping cart assistant. It works best when:

Connected to a real product catalog via the tool interface
The catalog supports text search (e.g., FAISS, Elasticsearch)
Products have variant information (color, size, etc.)
The conversation is multi-turn with tool execution between turns

Limitations

Requires external tool implementations — the model generates tool calls but does not execute them
Trained on English product data only
Variant matching depends on catalog quality — ambiguous product names may cause errors

Citation

If you use this model, please cite:

bibtex

@software{ecomrlve2026,
  title={EcomRLVE-GYM: Reinforcement Learning with Adaptive Verifiable Environments for E-Commerce},
  year={2026},
  url={https://github.com/owlgebra-ai/EcomRLVE-Gym}
}