SandyVeliz/acervo-extractor-v2
Acervo Extractor v2
A fine-tuned version of Qwen3.5-9B specialized in knowledge graph extraction from conversations. Given a conversation turn and existing graph context, the model outputs structured JSON with intent classification, topic detection, retrieval decision, entities, relations, and facts.
Base model: Qwen3.5-9B | Method: QLoRA (4-bit, r=16, alpha=32) | Training: ~1,000 examples, 3 epochs
Built for Acervo — a semantic compression layer for AI agents that replaces raw conversation history with compressed knowledge graph nodes.
Supersedes: acervo-extractor-qwen3.5-9b (v1, deprecated)
What's new in v2
v1 only handled topic detection and entity extraction. v2 adds intent classification and retrieval decision — two fields that were previously handled by regex/keyword heuristics outside the model.
Why intent matters
v1 benchmarks showed 78% intent accuracy — the model classified overview questions as specific (6 out of 9 failures). This cascaded: wrong intent led to wrong retrieval strategy (56% S2 accuracy) and wrong budget allocation (32% S3 accuracy).
v2 trains the model to classify intent directly, replacing the external regex classifier.
Why retrieval matters
The retrieval field tells the system whether to fetch full document chunks or just use node summaries:
summary_only— for overview questions, chat, conceptual queries (cheaper, faster)with_chunks— for code lookups, specific facts, detailed analysis (needs raw content)
Output schema
v1 output (deprecated)
{
"topic": {"action": "same|changed|subtopic", "label": "..."},
"entities": [...],
"relations": [...],
"facts": [...]
}v2 output (new fields highlighted)
{
"intent": "overview|specific|chat|followup", // NEW
"topic": {"action": "same|changed|subtopic", "label": "..."},
"retrieval": "summary_only|with_chunks", // NEW
"entities": [...],
"relations": [...],
"facts": [...]
}Intent types
Examples
Intent: overview
USER: What is this project about?{
"intent": "overview",
"topic": {"action": "same", "label": null},
"retrieval": "summary_only",
"entities": [],
"relations": [],
"facts": []
}Intent: specific (with extraction)
USER: Beacon ya tiene 50 mil usuarios y estamos migrando a Kubernetes.{
"intent": "specific",
"topic": {"action": "same", "label": null},
"retrieval": "with_chunks",
"entities": [
{
"id": "kubernetes",
"label": "Kubernetes",
"type": "technology",
"layer": "UNIVERSAL",
"attributes": {},
"facts": [],
"existing_id": null
}
],
"relations": [
{"source": "beacon", "target": "kubernetes", "relation": "uses_technology"}
],
"facts": [
{"entity": "beacon", "text": "Has 50,000 users", "speaker": "user"}
]
}Intent: chat (empty output)
USER: That's interesting, thanks!{
"intent": "chat",
"topic": {"action": "same", "label": null},
"retrieval": "summary_only",
"entities": [],
"relations": [],
"facts": []
}Intent: followup
PREVIOUS ASSISTANT: The auth module uses JWT tokens with 24-hour expiry.
USER: Tell me more about that.{
"intent": "followup",
"topic": {"action": "same", "label": null},
"retrieval": "with_chunks",
"entities": [],
"relations": [],
"facts": []
}Key capabilities
Training details
Dataset composition
Schema
Entity types (enum)
person, organization, project, technology, place, event, document, conceptRelation types (enum)
part_of, created_by, maintains, works_at, member_of,
uses_technology, depends_on, alternative_to,
located_in, deployed_on, produces, serves, documented_in,
participated_in, triggered_by, resulted_inLayers
- PERSONAL — user owns, created, or directly uses it
- UNIVERSAL — public knowledge (technologies, fictional characters, cities)
Usage
With LM Studio / Ollama (GGUF)
Download the GGUF file from the gguf/ folder and load in LM Studio. The model appears as acervo-extractor-v2.
With Transformers + LoRA
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-9B", device_map="auto")
model = PeftModel.from_pretrained(base_model, "SandyVeliz/acervo-extractor-v2")
tokenizer = AutoTokenizer.from_pretrained("SandyVeliz/acervo-extractor-v2")
messages = [
{"role": "system", "content": "You are a knowledge extractor for a personal knowledge graph. Analyze the conversation and return a single JSON object with: intent, topic, retrieval, entities, relations, and facts.\n\nIntent — classify the user's intent:\n- \"overview\": user wants a high-level summary, project description, general information, counts, or listings.\n- \"specific\": user wants a precise detail, specific code, a particular fact, or a specific section.\n- \"chat\": casual conversation, greetings, acknowledgments, opinions, or thanks.\n- \"followup\": continuing the previous topic with more depth, \"tell me more\", or referencing something just discussed.\n\nRetrieval — decide what data the system should fetch:\n- \"summary_only\": the node summary is enough (overview, chat, conceptual questions).\n- \"with_chunks\": the user needs specific content from documents (code lookups, specific facts, detailed analysis).\n\nOutput valid JSON only, no markdown, no explanation."},
{"role": "user", "content": "EXISTING NODES:\n[]\n\nTOPIC HINT: unresolved\nCURRENT TOPIC: null\n\nPREVIOUS ASSISTANT: null\nUSER: I work at Acme Corp building a React app called Beacon with PostgreSQL."}
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
outputs = model.generate(inputs.to(model.device), max_new_tokens=1024, temperature=0.1)
print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))With Unsloth (recommended for inference)
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
"SandyVeliz/acervo-extractor-v2",
max_seq_length=2048, load_in_4bit=True,
)
FastLanguageModel.for_inference(model)With Acervo (intended use)
from acervo import Acervo, OpenAIClient
llm = OpenAIClient(base_url="http://localhost:1234/v1", model="acervo-extractor-v2")
memory = Acervo(llm=llm, owner="user")Intended use
This model is designed as the extraction component inside Acervo, a semantic compression layer for AI agents. It replaces general-purpose LLM calls for topic detection, intent classification, and entity extraction with a specialized, faster model.
It can also be used standalone for:
- Building knowledge graphs from conversations
- Structured entity/relation extraction from text
- Topic detection in multi-turn dialogues
- Intent classification for conversational AI
- Retrieval strategy decisions (RAG pipelines)
Version history
License
Apache 2.0 — same as the base model.