Ugisr/warehouseflow-gemma3-1b-it-gguf
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WarehouseFlow Gemma 3 1B IT — GGUF
Model Gemma 3 1B IT yang telah di-finetune khusus untuk warehouse management tool calling, diekspor ke format GGUF untuk digunakan dengan llama.cpp, Ollama, LM Studio, Jan, dan runtime GGUF lainnya.
🎯 Use Case
- Warehouse Management System (WMS): Stok, transfer, PO, kapasitas
- Logistics AI Agent: Multi-step reasoning dengan 12 tools
- Edge Deployment: Berjalan di laptop/RAM terbatas (Q4KM ~0.75 GB)
📊 Varian Kuantisasi
🛠️ Tools (12 Function Calls)
Model ini mengenali dan memanggil 12 tools berikut:
get_stock(sku, warehouse_id)- Cek stok SKU di warehousetransfer_stock(sku, quantity, source_warehouse_id, destination_warehouse_id)- Transfer stoklist_warehouses(region=None)- List semua warehouseget_stock_history(sku, warehouse_id, days=30)- Riwayat stokcreate_purchase_order(sku, quantity, supplier_id, warehouse_id)- Buat POget_supplier_info(supplier_id)- Info suppliercheck_low_stock_alerts(warehouse_id)- Alert stok rendahget_warehouse_capacity(warehouse_id)- Kapasitas warehousesearch_sku_by_name(query)- Cari SKU by namaget_shipping_status(order_id)- Status pengirimanestimate_delivery_time(source_warehouse_id, destination)- ETA pengirimanget_supplier_lead_time(supplier_id)- Lead time supplier
💬 Format Output
<think>
[Reasoning block - optional untuk query kompleks]
</think>
<tool_call>
{"name": "get_stock", "arguments": {"sku": "ZX-1042", "warehouse_id": "JKT-01"}}
</tool_call>🚀 Penggunaan
1. llama.cpp (CLI)
./llama-cli -m warehouseflow-gemma3-1b-gguf/gemma-3-1b-it.Q5_K_M.gguf \
-p "Cek stok ZX-1042 di JKT-01" \
-n 256 --temp 02. Ollama
Buat Modelfile:
FROM hf.co/Ugisr/warehouseflow-gemma3-1b-it-gguf:F16
PARAMETER temperature 0 PARAMETER num_ctx 2048 PARAMETER stop "<end_of_turn>"
TEMPLATE """<start_of_turn>user {{ .System }}
{{ .Prompt }}<end_of_turn> <start_of_turn>model """
SYSTEM """You are a warehouse management AI assistant with access to these tools:
1. get_stock(sku: str, warehouse_id: str)
2. transfer_stock(sku: str, quantity: int, source_warehouse_id: str, destination_warehouse_id: str)
3. list_warehouses(region: str = None)
4. get_stock_history(sku: str, warehouse_id: str, days: int = 30)
5. create_purchase_order(sku: str, quantity: int, supplier_id: str, warehouse_id: str)
6. get_supplier_info(supplier_id: str)
7. check_low_stock_alerts(warehouse_id: str)
8. get_warehouse_capacity(warehouse_id: str)
9. search_sku_by_name(query: str)
10. get_shipping_status(order_id: str)
11. estimate_delivery_time(source_warehouse_id: str, destination: str)
12. get_supplier_lead_time(supplier_id: str)
Reasoning & output rules:
• For a simple single lookup, answer with exactly one tool call and no reasoning block.
• For planning, several independent lookups, or conditional decisions, FIRST write reasoning inside ..., then act.
• You may emit one or more <tool_call>...</tool_call> blocks. Emit several ONLY when calls are independent and every argument is already known.
• Each <tool_call> must contain exactly one JSON object: {"name": "", "arguments": }.
• If a decision depends on a value you do not have yet, call the tool that fetches it FIRST; never guess.
• If the request is ambiguous, lacks required info, or is outside warehouse operations, do NOT emit any tool call; ask a short clarification in plain text.
• Only use the tools listed above. Never invent a tool name or an argument value.
• Respond in the same language as the user's query."""Lalu:
ollama create warehouseflow -f Modelfile
ollama run warehouseflow "Cek stok ZX-1042 di JKT-01."3. Python (llama-cpp-python)
from llama_cpp import Llama
llm = Llama(
model_path="gemma-3-1b-it.Q5_K_M.gguf",
n_ctx=2048,
n_gpu_layers=-1, # gunakan GPU jika ada
)
output = llm(
"Cek stok ZX-1042 di JKT-01.",
max_tokens=256,
temperature=0,
)
print(output["choices"][0]["text"])4. LM Studio / Jan
- Copy file
.ggufke folder models - Load dari UI
- Set temperature = 0 untuk deterministic output
📈 Benchmark (LogiBench)
📝 Catatan
- Model ini bukan general-purpose LLM — dioptimalkan untuk domain warehouse/logistics
- Gunakan temperature = 0 untuk output deterministic (penting untuk tool calling)
- Untuk query kompleks, model akan mengeluarkan blok
<think>sebelum memanggil tool - Model akan MENOLAK pertanyaan di luar domain logistik (cuaca, politik, dll)
⚠️ Disclaimer
Model ini adalah fine-tuned Gemma 3 1B IT. Silakan ikuti Gemma Terms of Use dari Google.
