license, base_model, language, library_name, pipeline_tag, tags

license	base_model	language	library_name	pipeline_tag	tags
apache-2.0	Qwen3-8B	en	transformers	text-generation	qwen3 reasoning uncensored chain-of-thought math gsm8k unsloth maxzt

Roswaal-8B

Roswaal-8B is a full-parameter reasoning model built on top of a deeply uncensored Qwen3-8B and post-trained via Chain-of-Thought (CoT) distillation.

The result is a compact, fast, dramatically more capable 8B reasoning model that proves data quality beats brute-force volume. Headline capabilities:

• 🏆 Dominates benchmarks: Scores 87.64% exact_match on the full GSM8K test set (1,319 questions) using 5-shot evaluation — outperforming both its base model and heavily fine-tuned 50K-synthetic variants.
• 🧠 Advanced Chain-of-Thought: Strictly trained to deconstruct complex prompts, show its work step-by-step, and perform self-correction inside <think> blocks before outputting the final answer.
• ⚡ High-Efficiency Training: Trained locally on a single NVIDIA RTX 6000 Ada Generation (96GB VRAM) in just over an hour using Unsloth optimization.

Roswaal-8B is intentionally designed to engage seriously with technically demanding, multi-step logical and mathematical challenges without unnecessary refusals or boilerplate disclaimers.

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Benchmark Results

Evaluated on the full GSM8K test set (1,319 problems) using lm-evaluation-harness with a 5-shot prompt configuration (temperature=0.1 / low-temp reasoning).

Model	GSM8K Accuracy
Gemma 3 4B IT	89.2%
Qwen2.5 Coder 14B Instruct	88.7%
Phi-4-mini	88.6%
Roswaal-8B	87.64%
Qwen2.5 Coder 7B Instruct	86.7%
Phi 3.5 Mini Instruct	86.2%
Phi-3 Medium (4k-instruct)	85.2%
Gemma 2 9B	84.9%
Llama 3.1 8B Instruct	82.4%
Qwen3-8B	79.4%
Mistral-7B	77.9%
Llama 3.2 3B	77.7%

Roswaal-8B scores 8.24 p.p. above Qwen3-8B on GSM8K.

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Methodology: Why It Works

Unlike standard fine-tuning processes that attempt to map a question directly to an answer, Roswaal-8B was explicitly trained on ~20,000 highly curated Chain-of-Thought (CoT) sequences.

The training objective forces the model to:

1. Deconstruct complex prompts into smaller, actionable logical steps.
2. Self-correct during the generation phase (e.g., catching internal arithmetic errors before outputting the final answer).
3. Strictly isolate its internal monologue from the user-facing output using specialized structural tags.

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Hyperparameters

Parameter	SFT
Method	LoRA (16-bit)
LoRA rank (r)	16
LoRA alpha	32
LoRA targets	"q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"
Learning rate	1e-4
Scheduler	Cosine
Optimizer	adamw_8bit
Epochs	1
Batch size	8
Gradient accumulation	4
Max sequence length	2,048
Precision	bf16
Gradient checkpointing	Unsloth

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Prompt Format & Generation Strategy

Roswaal-8B relies on the standard ChatML template but requires a specific generation logic. The model expects to enclose its reasoning process inside <think>...</think> tags.

Recommended Generation Parameters:

• Temperature: 0.1 to 0.6 (Keep it low to prevent logical drift during complex math).
• Top_p: 0.9
• Max_new_tokens: 1024 - 4096 (Crucial: The model needs enough token space to "think" before answering. Do not restrict this too heavily).

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Developed by maxzt