# qwen2.5-7b-ben-franklin-v3c-factual-r8-qv-from-base ## Summary Custom Benjamin Franklin LoRA adapter for Qwen2.5 7B Instruct 4-bit. ## Paths - Portfolio copy: `adapters/qwen2.5-7b-ben-franklin-v3c-factual-r8-qv-from-base` - Source: copied adapter artifact in `adapters/qwen2.5-7b-ben-franklin-v3c-factual-r8-qv-from-base` - Base model: `unsloth/Qwen2.5-7B-Instruct-bnb-4bit` ## Adapter details - Adapter directory size: 0.08 GB - adapter_model.safetensors: 9.6 MB - SHA256(adapter_model.safetensors): `8f71e370eb4e7ddec058439e64cb608a54ebf4aad975a9a35154f6bf320f619a` - LoRA rank: 8 - LoRA alpha: 16 - Target modules: `['v_proj', 'q_proj']` ## Strengths - Largest Benjamin Franklin LoRA family proven trainable on this RTX 3070 8GB machine. - Best base reasoning/coherence among the local Franklin adapters. - Good short-turn continuity in the coherence benchmark. - Proves r=8 q/v LoRA from clean 7B base can train on this 8GB GPU. - Benchmark score: 28 with flags {'base_identity_leak': 1, 'overdenial': 1}. ## Weaknesses / caveats - Qwen2.5 7B base is already highly steerable, so improvements over the prompted base are modest. - q/v-only LoRA is weak for implanting stubborn factual corrections. - Craven Street/Hewson factuality remains unreliable unless retrieval/prompt context is supplied. - Regressed versus v1/v2/v3 in the coherence benchmark. ## Data mix - franklin_qwen3_8b_chatml_answer_clean.jsonl: 2400 rows - franklin_7b_factual_coherence_repair_v3.jsonl: 308 rows ## Performance Score: 28 Flags: `{'base_identity_leak': 1, 'overdenial': 1}` Raw benchmark: benchmarks/franklin_coherence/franklin_coherence_20260623_084048.json HTML: benchmarks/franklin_coherence/franklin_coherence_20260623_084048.html ## Memory / compute requirements Inference: RTX 3070 8GB works in 4-bit with the adapter; expect roughly 6-7GB VRAM. Training proven only with q_proj/v_proj, r=4 or r=8, max_seq_length=512, batch_size=1, gradient_accumulation=16; full-module LoRA is not recommended on 8GB. ## Possible project uses - Conversational Franklin persona in local apps. - A strong baseline for RAG-backed historical character demos. - Best starting point for further 7B experiments.