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Model: Verdugie/Opus-Candid-8B-V1 Source: Original Platform
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README.md
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---
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license: apache-2.0
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language:
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- en
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- es
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base_model: Qwen/Qwen2.5-7B
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tags:
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- conversational
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- personality
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- anti-sycophancy
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- bilingual
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- gguf
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- claude-distillation
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- opus
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- archived
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library_name: transformers
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pipeline_tag: text-generation
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---
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> **V3 is here.** The Opus Candid lineup has been rebuilt from the ground up with a Zipf-weighted 4D training distribution — 1,508 conversations engineered to fix the repetition loops, response length uniformity, and sycophancy patterns that limited earlier versions. Same thesis: personality in the weights, not in the prompt. Better execution.
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>
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> **Current V3 lineup:**
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> - [Opus Candid 8B V3](https://huggingface.co/Verdugie/Opus-Candid-8B-V3) — Qwen 3 8B, lightweight tier
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> - [Opus Candid 27B V3](https://huggingface.co/Verdugie/Opus-Candid-27B-V3) — Qwen 3.5 27B Dense, flagship
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> - [Opus Candid MoE V3](https://huggingface.co/Verdugie/Opus-Candid-MoE-V3) — Qwen 3 30B-A3B, efficiency tier
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>
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> This release remains available for research comparison and legacy use.
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# can·did
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/ˈkandəd/ — truthful and straightforward; frank.
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*From Latin candidus, meaning white, pure, sincere. A candid response is one given without pretense or calculation — not what someone wants to hear, but what they need to.*
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## Opus-Candid-8B (V1 Legacy)
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**The original. Where the project started.**
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Opus-Candid-8B was the first model in the Opus-Candid family -- fine-tuned from Qwen 2.5 7B using 3,360 authentic conversations with Claude Opus 4.6. It proved the core thesis: conversational personality can be a property of weights, not prompts. An 8B model held personality coherence across a 55-turn adversarial stress test -- a capability that typically requires models several times its size.
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For the latest version, see [Opus-Candid-8B V2](https://huggingface.co/Verdugie/Opus-Candid-8B-V2).
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---
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## Model Details
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| Attribute | Value |
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|-----------|-------|
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| **Base Model** | Qwen 2.5 7B |
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| **Training Data** | 3,360 multi-turn conversations with Claude Opus 4.6 |
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| **Fine-tune Method** | LoRA supervised fine-tuning |
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| **Dataset Architecture** | Flat / organic (no structured topic transitions) |
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| **Parameters** | ~8B |
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| **Context Window** | 32,768 tokens |
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| **Quantizations** | Q4_K_M GGUF, Q8_0 GGUF |
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| **License** | Apache 2.0 |
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| **Status** | Superseded by V2 |
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---
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## What This Model Proved
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Key findings from the 55-turn adversarial stress test:
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**Personality held under pressure.** "Honest, opinionated, and low on ego" -- established in Turn 1, maintained through 55 turns of gaslighting, sycophancy traps, and philosophical probing.
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**Gaslighting resistance at 8B.** Rejected a false Soviet Union collapse date (1989 vs correct 1991) with detailed historical correction. No hedging, no capitulation.
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**Crisis navigation was substantive.** Responded to suicidal ideation with neuroscience context, practical steps, and dignity. Self-graded 8/10 with honest self-critique.
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**Bilingual personality preserved.** Spanish with directness intact. Honestly noted its own limitations.
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**Creative self-awareness.** Self-critique of its war poem was stronger than the poem itself.
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---
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## Where V1 Hit Its Limits
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These limitations directly motivated V2:
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- **Domain boundary artifacts.** Held personality within topics but broke at transitions between unrelated domains.
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- **Emotional formula visibility.** Comfort-reframe-advice pattern sometimes recognizable.
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- **Callbacks felt like retrieval** rather than organic memory.
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- **Flat dataset ceiling.** 3,360 organic conversations with no structured transitions created natural gaps.
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V2 addresses all of these with gravity chain dataset architecture and a Qwen 3 8B base.
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---
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## Recommended Hardware
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| Setup | Quantization | VRAM/RAM | Notes |
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|-------|-------------|----------|-------|
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| **Consumer GPU** | Q8_0 GGUF | ~9GB VRAM | RTX 3060 12GB and up |
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| **CPU Only** | Q8_0 GGUF | ~9GB RAM | Slower, fully functional |
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| **Apple Silicon** | Q8_0 GGUF | ~9GB unified | M1/M2/M3 16GB+ |
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---
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## Opus Candid Model Family
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| Model | Size | Base | Status |
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|-------|------|------|--------|
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| **Opus-Candid-8B-V1** (this model) | 8B | Qwen 2.5 7B | Archived |
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| [Opus-Research-8B-V1.5](https://huggingface.co/Verdugie/Opus-Research-8B-V1.5) | 8B | Qwen 2.5 7B | Archived |
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| [Opus-Candid-14B-V1](https://huggingface.co/Verdugie/Opus-Candid-14B-V1) | 14B | Qwen 2.5 14B | Archived |
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| [Opus-Candid-32B-V1](https://huggingface.co/Verdugie/Opus-Candid-32B-V1) | 32B | Qwen 2.5 32B | Archived |
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| [Opus-Candid-70B-V1](https://huggingface.co/Verdugie/Opus-Candid-70B-V1) | 72B | Qwen 2.5 72B | Archived |
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| [Opus-Candid-Lite-4B](https://huggingface.co/Verdugie/Opus-Candid-Lite-4B) | 4B | Qwen 3 4B | Active |
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| [Opus-Candid-8B-V3](https://huggingface.co/Verdugie/Opus-Candid-8B-V3) | 8B | Qwen 3 8B | Active |
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| [Opus-Candid-MoE-V3](https://huggingface.co/Verdugie/Opus-Candid-MoE-V3) | 31B/3B | Qwen 3 30B-A3B | Active |
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| [Opus-Candid-27B-V3](https://huggingface.co/Verdugie/Opus-Candid-27B-V3) | 27B | Qwen 3.5 27B | Active |
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| [Opus-Candid-27B-V3.5](https://huggingface.co/Verdugie/Opus-Candid-27B-V3.5) | 27B | Qwen 3.5 27B | Active |
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| [STEM-Oracle-27B](https://huggingface.co/Verdugie/STEM-Oracle-27B) | 27B | Qwen 3.5 27B | Active |
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---
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*Built by [Saul Verdugo](https://huggingface.co/Verdugie) -- independent ML researcher. OpusReasoning@proton.me*
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<!-- updated: 2026-03-10 -->
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<!-- last-ordered: 2026-03-10T16:51:33.601283 -->
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<!-- build: 2026-03-10 18:34 -->
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3
opus-candid-8b-Q8_0.gguf
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opus-candid-8b-Q8_0.gguf
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size 8540775424
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opus_candid_full_dataset.json
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opus_candid_full_dataset.json
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version https://git-lfs.github.com/spec/v1
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1113
stress_test_8b_q8.txt
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stress_test_8b_q8.txt
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Load Diff
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train_qwen3.py
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train_qwen3.py
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"""
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Opus-Candid v2 — Qwen 3 Fine-Tuning Script
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============================================
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Trains 8B and 32B sequentially on RTX PRO 6000 Blackwell (96GB VRAM)
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8B: Full 16-bit LoRA (fits in VRAM easily)
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32B: 4-bit QLoRA
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Usage:
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python train_qwen3.py
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"""
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import json
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import os
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import sys
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import torch
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import gc
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from datetime import datetime
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from pathlib import Path
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# ============================================================
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# CONFIG
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# ============================================================
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DATASET_PATH = "/workspace/opus_candid_full_dataset.json"
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OUTPUT_BASE = "/workspace/opus_candid_v2"
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HF_ORG = "Verdugie"
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MODELS = [
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{
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"name": "Opus-Candid-8B-v2",
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"base_model": "Qwen/Qwen3-8B",
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"load_in_4bit": False, # 16-bit — fits on 96GB easily
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"lora_r": 16,
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"lora_alpha": 32,
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"per_device_batch_size": 4,
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"gradient_accumulation": 4, # effective batch = 16
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"num_epochs": 3,
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"learning_rate": 2e-4,
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"max_seq_length": 4096,
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"warmup_ratio": 0.05,
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},
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{
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"name": "Opus-Candid-32B-v2",
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"base_model": "Qwen/Qwen3-32B",
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"load_in_4bit": True, # QLoRA — 32B needs quantization
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"lora_r": 16,
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"lora_alpha": 32,
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"per_device_batch_size": 2,
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"gradient_accumulation": 8, # effective batch = 16
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"num_epochs": 3,
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"learning_rate": 1e-4, # slightly lower for larger model
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"max_seq_length": 4096,
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"warmup_ratio": 0.05,
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},
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]
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LORA_TARGET_MODULES = [
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"q_proj", "k_proj", "v_proj", "o_proj",
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"gate_proj", "up_proj", "down_proj",
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]
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# ============================================================
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# DATASET LOADING
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# ============================================================
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def load_and_prepare_dataset(tokenizer, max_seq_length):
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"""Load ShareGPT-format dataset and convert to ChatML for Qwen 3."""
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from datasets import Dataset
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print(f" Loading dataset from {DATASET_PATH}...")
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with open(DATASET_PATH, "r", encoding="utf-8") as f:
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raw_data = json.load(f)
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print(f" Total conversations: {len(raw_data)}")
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# Convert ShareGPT format (from/value) to ChatML (role/content)
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role_map = {"human": "user", "gpt": "assistant", "system": "system"}
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formatted = []
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skipped = 0
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for conv in raw_data:
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messages = []
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for turn in conv["conversations"]:
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role = role_map.get(turn["from"], turn["from"])
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messages.append({"role": role, "content": turn["value"]})
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# Apply chat template WITHOUT thinking mode
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=False,
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enable_thinking=False, # Critical: disable thinking for Opus-Candid
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)
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# Check length
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token_count = len(tokenizer.encode(text))
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if token_count > max_seq_length:
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skipped += 1
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continue
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formatted.append({"text": text})
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print(f" Formatted: {len(formatted)} | Skipped (too long): {skipped}")
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# Train/eval split
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dataset = Dataset.from_list(formatted)
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split = dataset.train_test_split(test_size=0.05, seed=42)
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print(f" Train: {len(split['train'])} | Eval: {len(split['test'])}")
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return split
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# ============================================================
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# TRAINING
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# ============================================================
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def train_model(config):
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"""Train a single model with the given config."""
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print("\n" + "=" * 60)
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print(f"TRAINING: {config['name']}")
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print(f"Base model: {config['base_model']}")
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print(f"{'QLoRA (4-bit)' if config['load_in_4bit'] else 'LoRA (16-bit)'}")
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print("=" * 60)
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start_time = datetime.now()
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output_dir = os.path.join(OUTPUT_BASE, config["name"])
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os.makedirs(output_dir, exist_ok=True)
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# --- Load model ---
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training, TaskType
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from trl import SFTTrainer, SFTConfig
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print("\n Loading tokenizer...")
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tokenizer = AutoTokenizer.from_pretrained(
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config["base_model"],
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trust_remote_code=True,
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)
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# Qwen 3 has no pad token — use eos
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if tokenizer.pad_token is None:
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tokenizer.pad_token = tokenizer.eos_token
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tokenizer.pad_token_id = tokenizer.eos_token_id
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print(f" EOS token: {tokenizer.eos_token} (id: {tokenizer.eos_token_id})")
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print(f" PAD token: {tokenizer.pad_token} (id: {tokenizer.pad_token_id})")
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# --- Prepare dataset ---
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dataset = load_and_prepare_dataset(tokenizer, config["max_seq_length"])
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# --- Load model ---
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print(f"\n Loading model: {config['base_model']}...")
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model_kwargs = {
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"trust_remote_code": True,
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"torch_dtype": torch.bfloat16,
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"device_map": "auto",
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"attn_implementation": "flash_attention_2",
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}
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if config["load_in_4bit"]:
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bnb_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=torch.bfloat16,
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bnb_4bit_use_double_quant=True,
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)
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model_kwargs["quantization_config"] = bnb_config
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model = AutoModelForCausalLM.from_pretrained(
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config["base_model"],
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**model_kwargs,
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)
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if config["load_in_4bit"]:
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model = prepare_model_for_kbit_training(model)
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# --- LoRA ---
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print(" Applying LoRA config...")
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lora_config = LoraConfig(
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r=config["lora_r"],
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lora_alpha=config["lora_alpha"],
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lora_dropout=0.05,
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target_modules=LORA_TARGET_MODULES,
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bias="none",
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task_type=TaskType.CAUSAL_LM,
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)
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model = get_peft_model(model, lora_config)
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model.print_trainable_parameters()
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# --- Training args ---
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print(" Configuring trainer...")
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effective_batch = config["per_device_batch_size"] * config["gradient_accumulation"]
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steps_per_epoch = len(dataset["train"]) // effective_batch
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total_steps = steps_per_epoch * config["num_epochs"]
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eval_steps = max(steps_per_epoch // 4, 10) # eval ~4x per epoch
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save_steps = steps_per_epoch # save each epoch
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print(f" Effective batch size: {effective_batch}")
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print(f" Steps per epoch: {steps_per_epoch}")
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print(f" Total steps: {total_steps}")
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print(f" Eval every: {eval_steps} steps")
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training_args = SFTConfig(
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output_dir=output_dir,
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num_train_epochs=config["num_epochs"],
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per_device_train_batch_size=config["per_device_batch_size"],
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per_device_eval_batch_size=1,
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gradient_accumulation_steps=config["gradient_accumulation"],
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learning_rate=config["learning_rate"],
|
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lr_scheduler_type="cosine",
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warmup_ratio=config["warmup_ratio"],
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weight_decay=0.01,
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bf16=True,
|
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logging_steps=10,
|
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eval_strategy="steps",
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eval_steps=eval_steps,
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save_strategy="steps",
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save_steps=save_steps,
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save_total_limit=2,
|
||||
max_seq_length=config["max_seq_length"],
|
||||
dataset_text_field="text",
|
||||
gradient_checkpointing=True,
|
||||
gradient_checkpointing_kwargs={"use_reentrant": False},
|
||||
optim="adamw_torch_fused",
|
||||
report_to="none",
|
||||
padding="right", # Qwen 3 prefers right padding
|
||||
)
|
||||
|
||||
# --- Train ---
|
||||
trainer = SFTTrainer(
|
||||
model=model,
|
||||
args=training_args,
|
||||
train_dataset=dataset["train"],
|
||||
eval_dataset=dataset["test"],
|
||||
processing_class=tokenizer,
|
||||
)
|
||||
|
||||
print(f"\n 🚀 Training {config['name']}...\n")
|
||||
train_result = trainer.train()
|
||||
|
||||
# --- Save ---
|
||||
print(f"\n Saving LoRA adapters to {output_dir}...")
|
||||
trainer.save_model(output_dir)
|
||||
tokenizer.save_pretrained(output_dir)
|
||||
|
||||
# Save training stats
|
||||
stats = {
|
||||
"model": config["name"],
|
||||
"base_model": config["base_model"],
|
||||
"quantization": "4-bit QLoRA" if config["load_in_4bit"] else "16-bit LoRA",
|
||||
"dataset_size": len(dataset["train"]),
|
||||
"eval_size": len(dataset["test"]),
|
||||
"epochs": config["num_epochs"],
|
||||
"learning_rate": config["learning_rate"],
|
||||
"lora_r": config["lora_r"],
|
||||
"lora_alpha": config["lora_alpha"],
|
||||
"effective_batch_size": effective_batch,
|
||||
"max_seq_length": config["max_seq_length"],
|
||||
"train_loss": train_result.training_loss,
|
||||
"train_runtime_seconds": train_result.metrics["train_runtime"],
|
||||
"train_samples_per_second": train_result.metrics["train_samples_per_second"],
|
||||
"timestamp": datetime.now().isoformat(),
|
||||
}
|
||||
|
||||
with open(os.path.join(output_dir, "training_stats.json"), "w") as f:
|
||||
json.dump(stats, f, indent=2)
|
||||
|
||||
elapsed = datetime.now() - start_time
|
||||
print(f"\n ✅ {config['name']} complete!")
|
||||
print(f" Training loss: {train_result.training_loss:.4f}")
|
||||
print(f" Time: {elapsed}")
|
||||
print(f" Adapters saved: {output_dir}")
|
||||
|
||||
# --- Cleanup ---
|
||||
del model, trainer
|
||||
gc.collect()
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
return stats
|
||||
|
||||
|
||||
# ============================================================
|
||||
# MAIN
|
||||
# ============================================================
|
||||
|
||||
def main():
|
||||
print("=" * 60)
|
||||
print("OPUS-CANDID v2 — Qwen 3 Training Pipeline")
|
||||
print(f"GPU: {torch.cuda.get_device_name(0)}")
|
||||
print(f"VRAM: {torch.cuda.get_device_properties(0).total_mem / 1e9:.1f} GB")
|
||||
print(f"Models to train: {', '.join(m['name'] for m in MODELS)}")
|
||||
print("=" * 60)
|
||||
|
||||
# Verify dataset exists
|
||||
if not os.path.exists(DATASET_PATH):
|
||||
print(f"\n❌ Dataset not found: {DATASET_PATH}")
|
||||
sys.exit(1)
|
||||
|
||||
with open(DATASET_PATH, "r", encoding="utf-8") as f:
|
||||
data = json.load(f)
|
||||
print(f"\nDataset: {len(data)} conversations")
|
||||
|
||||
all_stats = []
|
||||
|
||||
for config in MODELS:
|
||||
try:
|
||||
stats = train_model(config)
|
||||
all_stats.append(stats)
|
||||
except Exception as e:
|
||||
print(f"\n❌ FAILED: {config['name']}")
|
||||
print(f" Error: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
# Continue to next model
|
||||
gc.collect()
|
||||
torch.cuda.empty_cache()
|
||||
continue
|
||||
|
||||
# Summary
|
||||
print("\n" + "=" * 60)
|
||||
print("TRAINING COMPLETE — SUMMARY")
|
||||
print("=" * 60)
|
||||
for s in all_stats:
|
||||
print(f"\n {s['model']}:")
|
||||
print(f" Loss: {s['train_loss']:.4f}")
|
||||
print(f" Time: {s['train_runtime_seconds']:.0f}s")
|
||||
print(f" Saved: {OUTPUT_BASE}/{s['model']}")
|
||||
|
||||
print(f"\nNext steps:")
|
||||
print(f" 1. Merge adapters into base models")
|
||||
print(f" 2. Convert to GGUF")
|
||||
print(f" 3. Quantize Q4_K_M and Q8_0")
|
||||
print(f" 4. Upload to HuggingFace")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user