Ik Belge Asistani Qwen3.5 4b

--- license: apache-2.0 base_model: unsloth/Qwen3.5-4B language: - en - tr tags: - text-generation-inference - transformers - unsloth - qwen3_5 - gguf - human-resources - structured-output - lora pipeline_tag: text-generation --- # İK Belge Asistanı — HR Document Structuring Assistant A Qwen3.5-4B fine-tune that turns messy, free-form HR notes into clean, structured JSON — in **Turkish or English**, matching whatever language the input is written in. **[Try the live demo →](https://huggingface.co/spaces/mustafabasar/ik-belge-asistani-demo)** ## What it does HR teams take raw notes during disciplinary meetings, ethics hotline calls, internal audits, and performance reviews. This model structures those notes into the exact JSON fields an HR system needs, without inventing details that weren't actually stated. Four supported document types: | Type | Turkish name | Example use case | |---|---|---| | Disciplinary record | Disiplin tutanağı | Structuring a workplace incident interview | | Ethics hotline report | Etik hattı bildirimi | Anonymized whistleblower report intake | | Internal audit finding | İç denetim bulgusu | Turning an auditor's raw note into a findings record | | Performance consistency check | Performans tutarlılık kontrolü | Flagging inconsistencies in review notes | ## Key properties - **Bilingual output matching**: writes Turkish JSON for Turkish input, English JSON for English input. - **Hallucination-averse**: trained to state only what the note actually says, and to flag anything unverified rather than assert it as fact. - **Privacy-conscious by default**: prefers job title/role over personal name for unverified third parties (witnesses, observers) unless the schema specifically requires identifying the accused/reported party. - **Small and CPU-deployable**: 4B parameters, ships as a Q4_K_M GGUF that runs comfortably on CPU-only hardware (see the [live demo](https://huggingface.co/spaces/mustafabasar/ik-belge-asistani-demo), which runs on free CPU infrastructure). ## Usage ### llama.cpp / llama-cpp-python (recommended — this is what the live demo uses) ```python from llama_cpp import Llama from transformers import AutoTokenizer REPO_ID = "mustafabasar/ik-belge-asistani-qwen3.5-4b" llm = Llama.from_pretrained( repo_id=REPO_ID, filename="ik-belge-asistani-qwen3.5-4b.Q4_K_M.gguf", n_ctx=4096, n_gpu_layers=0, # CPU-...

Source context: 126 downloads · 0 likes · Pipeline text-generation · Library transformers · Repo mustafabasar/ik-belge-asistani-qwen3.5-4b