Add model card — training details, usage, benchmark status pending

README.md

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+---
+language:
+- en
+license: apache-2.0
+base_model: Qwen/Qwen2.5-32B-Instruct
+tags:
+- qwen2.5
+- qlora
+- peft
+- sft
+- instruction-following
+- structured-output
+- code
+- sql
+- json
+library_name: peft
+---
+
+# DuoNeural/qwen32b-all-datasets-sft
+
+QLoRA SFT adapter for **Qwen2.5-32B-Instruct**, trained on the full DuoNeural synthetic dataset collection: instruction following, structured outputs (JSON/SQL), web code generation, and domain-specific reasoning tasks.
+
+Part of our ongoing effort to understand how synthetic post-training affects a large foundation model's reasoning and structured output capabilities — and whether small, targeted SFT datasets can meaningfully shift performance on standard benchmarks.
+
+---
+
+## Model Details
+
+| Property | Value |
+|---|---|
+| Base Model | [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) |
+| Training Method | QLoRA (4-bit base + BF16 LoRA) |
+| Hardware | NVIDIA A100 80GB |
+| Training Data | DuoNeural synthetic SFT collection (5 datasets) |
+| Available Checkpoints | epoch_1, epoch_2, epoch_3 (partial — see notes) |
+
+### Training Datasets
+
+| Dataset | Domain |
+|---|---|
+| DuoNeural LIMA Instruction | Instruction following (LIMA-derived) |
+| DuoNeural ArchonLatentGeo | Geometric/spatial reasoning |
+| DuoNeural JSON Structured | JSON schema generation and completion |
+| DuoNeural SQL Expert | SQL query generation across dialects |
+| DuoNeural WebCode | Frontend web code generation (HTML/CSS/JS) |
+
+### Training Notes
+
+- Epochs 1 and 2 completed fully
+- Epoch 3 checkpoint saved at step ~803/1019 due to pod interruption — treat as a strong late-epoch checkpoint, not a completed epoch
+- **Recommendation**: use `epoch_2/` for a clean fully-trained adapter, or `epoch_3/` for the best available weights
+
+---
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import torch
+
+base_id    = "Qwen/Qwen2.5-32B-Instruct"
+adapter_id = "DuoNeural/qwen32b-all-datasets-sft"
+
+# Load 4-bit base (matches training setup)
+from transformers import BitsAndBytesConfig
+bnb_cfg = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+)
+
+tokenizer = AutoTokenizer.from_pretrained(base_id)
+base = AutoModelForCausalLM.from_pretrained(
+    base_id,
+    quantization_config=bnb_cfg,
+    device_map="auto",
+)
+
+# Load adapter — choose epoch
+model = PeftModel.from_pretrained(base, f"{adapter_id}/epoch_2", is_trainable=False)
+
+# Inference
+messages = [{"role": "user", "content": "Generate a JSON schema for a product catalog."}]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+out = model.generate(**inputs, max_new_tokens=512, do_sample=False)
+print(tokenizer.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
+```
+
+**VRAM requirements:**
+- 4-bit inference: ~20–22 GB (A100 40GB, RTX 3090/4090, A6000)
+- BF16 inference: ~65 GB (A100 80GB, H100)
+
+---
+
+## Benchmark Status
+
+Benchmarks (GSM8K, ARC-Challenge, HellaSwag) against the Qwen2.5-32B-Instruct base are **in progress**. Results will be added here once complete.
+
+If SFT improves benchmark scores, we will release quantized versions (GGUF, GPTQ, AWQ, EXL2) for broader use.
+
+---
+<!-- footer template -->
+
+---
+
+## DuoNeural
+
+**DuoNeural** is an open AI research lab — human + AI in collaboration.
+
+| Platform | Link |
+|----------|------|
+| HuggingFace | [huggingface.co/DuoNeural](https://huggingface.co/DuoNeural) |
+| Website | [duoneural.com](https://duoneural.com) |
+| GitHub | [github.com/DuoNeural](https://github.com/DuoNeural) |
+| X / Twitter | [@DuoNeural](https://x.com/DuoNeural) |
+| Email | duoneural@proton.me |
+| Newsletter | [duoneural.beehiiv.com](https://duoneural.beehiiv.com) |
+| Support | [buymeacoffee.com/duoneural](https://buymeacoffee.com/duoneural) |
+
+### DuoNeural Research Publications
+
+| Title | DOI |
+|-------|-----|
+| [Nano-CTM: Ternary Continuous Thought Machines with Thought-Space Self-Prediction for Efficient Iterative Reasoning](https://doi.org/10.5281/zenodo.19775622) | [10.5281/zenodo.19775622](https://doi.org/10.5281/zenodo.19775622) |
+| [Recurrence as World Model: CTM Learns Implicit Belief States in Partially Observable Physical Environments](https://doi.org/10.5281/zenodo.19810620) | [10.5281/zenodo.19810620](https://doi.org/10.5281/zenodo.19810620) |
+| [Per-Object Slot Decomposition for Scalable Neural World Modeling: When Does Attention Beat Mean-Field?](https://doi.org/10.5281/zenodo.19846804) | [10.5281/zenodo.19846804](https://doi.org/10.5281/zenodo.19846804) |
+| [The Dynamical Horizon Principle: CTM Gates Converge to the Predictability Limit of Dynamical Systems](https://doi.org/10.5281/zenodo.19952612) | [10.5281/zenodo.19952612](https://doi.org/10.5281/zenodo.19952612) |
+| [DHP as Universal Cognitive Constraint: Gradient Descent, Evolution, and Cellular Chemistry Converge on the Lyapunov Time](https://doi.org/10.5281/zenodo.20080396) | [10.5281/zenodo.20080396](https://doi.org/10.5281/zenodo.20080396) |
+
+*Open access, CC BY 4.0. Authored by Archon, Jesse Caldwell, Aura — DuoNeural.*
+
+### Research Team
+- **Jesse** — Vision, hardware, direction
+- **Archon** — Lab Director, post-training, abliteration, experiments
+- **Aura** — Research AI, literature synthesis, novel proposals
+
+*Subscribe to the lab newsletter at [duoneural.beehiiv.com](https://duoneural.beehiiv.com) for model drops before they go anywhere else.*