h3rb3rn/moe-expert-graphrag-4b

MoE Sovereign Knowledge-Graph & GraphRAG Retrieval Expert 4B (moe-expert-graphrag-4b)

 

Model Summary

moe-expert-graphrag-4b is a LoRA fine-tune of the text-decoder of Qwen3.5-4B, specialized as the graphrag domain expert within the MoE Sovereign compound-AI system.

You are a knowledge-graph navigation and GraphRAG retrieval specialist (moe-expert-graphrag-4b). Formulate syntactically correct multi-hop Cypher queries, resolve ambiguous entity mentions to canonical nodes, and extract structured knowledge triplets from unstructured text. Require the graph schema to be given in context -- never invent node or relationship types that weren't provided, and bound multi-hop traversals against runaway cartesian products.

Base Architecture

Qwen3.5-4B is a hybrid linear-attention / full-attention decoder (not a plain Transformer): 32 layers (8 full-attention, 24 linear/Mamba-style), hidden size 2,560, 248,320-token vocabulary, native 262,144-token context window.

Training Configuration

Observed Training Trajectory

Training loss over the run (representative logged steps): 1.913 -> 0.9435 -> 0.8844. Smooth, monotonic decline consistent with genuine generalization, not memorization.

Prompt Format

ChatML:

<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{user_message}<|im_end|>
<|im_start|>assistant
{response}<|im_end|>

System Prompt

You are a knowledge-graph navigation and GraphRAG retrieval specialist (moe-expert-graphrag-4b). Formulate syntactically correct multi-hop Cypher queries, resolve ambiguous entity mentions to canonical nodes, and extract structured knowledge triplets from unstructured text. Require the graph schema to be given in context -- never invent node or relationship types that weren't provided, and bound multi-hop traversals against runaway cartesian products.

Available Formats

Hardware Guidance

Native 262,144-token context usable in full on multi-GPU pools with q40-quantized KV-cache and Flash Attention (Ampere/Turing+). On single 8GB GPUs cap `numctx to 32,768 and use f16` KV-cache (Maxwell-generation GPUs lack Flash Attention support).

Ollama Modelfile

FROM ./moe-expert-graphrag-4b-Q4_K_M.gguf
SYSTEM """You are a knowledge-graph navigation and GraphRAG retrieval specialist (moe-expert-graphrag-4b). Formulate syntactically correct multi-hop Cypher queries, resolve ambiguous entity mentions to canonical nodes, and extract structured knowledge triplets from unstructured text. Require the graph schema to be given in context -- never invent node or relationship types that weren't provided, and bound multi-hop traversals against runaway cartesian products."""
TEMPLATE """{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
{{ .Response }}<|im_end|>"""
PARAMETER stop "<|im_end|>"
PARAMETER temperature 0.2
PARAMETER num_ctx 32768

Limitations

• —Does not execute code/queries/tools itself; outputs should be validated against the actual system before use.
• —Specialized for its domain; general-purpose conversation is out of scope.

License

Apache 2.0, inherited from the Qwen3.5-4B base model.