初始化项目，由ModelHub XC社区提供模型

Model: Bloce3an/qwen2.5-0.5B-entities-relationship-gguf
Source: Original Platform

README.md

@@ -0,0 +1,69 @@
+---
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+library_name: gguf
+pipeline_tag: text-generation
+tags:
+- gguf
+- qwen
+- llama.cpp
+- knowledge-graph
+- information-extraction
+- unsloth
+license: apache-2.0
+language:
+- en
+---
+
+# Qwen2.5-0.5B-Instruct Knowledge Graph Extractor (GGUF)
+
+This is a GGUF quantized version of the `qwen2.5-0.5B-kg-lora2` model. The base model (`Qwen2.5-0.5B-Instruct`) was fine-tuned with Unsloth to strictly extract Knowledge Graph triples from unstructured text.
+
+The weights have been merged and quantized to allow for fast, lightweight inference on CPU and edge devices using tools like [llama.cpp](https://github.com/ggerganov/llama.cpp), [Ollama](https://ollama.com/), or [LM Studio](https://lmstudio.ai/).
+
+## Available Quantizations
+This repository contains the following quantized files:
+- **`Q8_0`**: 8-bit quantization. Highest quality, nearly identical to the original fp16 model. Good if you have enough RAM.
+- **`Q6_K`**: 6-bit quantization. Excellent balance between size and quality.
+- **`Q4_K_M`**: 4-bit quantization. Recommended for most users. Fast inference and very low memory footprint with minimal quality loss.
+
+## Prompt Format (CRITICAL)
+This model was strictly fine-tuned on a specific ChatML system prompt. **You MUST use this exact system prompt** or the model will hallucinate or output the wrong format.
+
+```text
+<|im_start|>system
+You are an expert at extracting clean, accurate knowledge graph triples from text.
+
+Your task is to carefully read the input text and extract **all** meaningful triples in this exact format:
+(subject | relation | object)
+
+Strict rules you must follow:
+- Subject and object must be specific named entities or concrete concepts explicitly mentioned in the text (people, organizations, locations, events, products, years, etc.)
+- Relation should be a short, clear predicate in base form or simple present tense (examples: "is", "has", "works at", "located in", "born in", "capital of", "founded in")
+- Only extract triples that are **directly supported** by the text — do **not** infer, assume, hallucinate or add information that is not clearly stated
+- If uncertain about a triple → do **not** include it
+- Each triple must be written on its **own separate line**
+- Do **not** add any explanations, headings, numbering, bullet points, comments, or extra text of any kind
+- If no valid triples can be extracted → return exactly one line: "No triples found"<|im_end|>
+<|im_start|>user
+Text:
+{your_input_text}<|im_end|>
+<|im_start|>assistant
+```
+
+## Usage with llama.cpp
+
+Once you have downloaded the `.gguf` file of your choice (e.g. `model-unsloth.Q4_K_M.gguf`), you can run it via `llama.cpp` using the CLI. Since Qwen2.5 uses ChatML, ensure that you pass the exact system instruction.
+
+```bash
+./main -m model-unsloth.Q4_K_M.gguf \
+  --color \
+  -c 2048 \
+  -temp 0.1 \
+  --repeat_penalty 1.15 \
+  -p "<|im_start|>system\nYou are an expert at extracting clean, accurate knowledge graph triples from text.\n\nYour task is to carefully read the input text and extract **all** meaningful triples in this exact format:\n(subject | relation | object)\n\nStrict rules you must follow:\n- Subject and object must be specific named entities or concrete concepts explicitly mentioned in the text (people, organizations, locations, events, products, years, etc.)\n- Relation should be a short, clear predicate in base form or simple present tense (examples: \"is\", \"has\", \"works at\", \"located in\", \"born in\", \"capital of\", \"founded in\")\n- Only extract triples that are **directly supported** by the text — do **not** infer, assume, hallucinate or add information that is not clearly stated\n- If uncertain about a triple → do **not** include it\n- Each triple must be written on its **own separate line**\n- Do **not** add any explanations, headings, numbering, bullet points, comments, or extra text of any kind\n- If no valid triples can be extracted → return exactly one line: \"No triples found\"<|im_end|>\n<|im_start|>user\nText:\nThe Tasmanian Devil is a carnivorous marsupial of the family Dasyuridae.<|im_end|>\n<|im_start|>assistant\n"
+```
+
+## Intended Use
+- Local processing of sensitive documents.
+- Rapid edge-device extraction of explicit Entity-Relation-Entity relationships.
+- Pipeline integration for RAG (Retrieval-Augmented Generation) graph curation.