GenueAI/Inelly-4.5-Blaze

Inelly 4.5 Blaze

Model Description

Inelly 4.5 Blaze is a fine-tuned version of Qwen2.5-1.5B-Instruct, trained on a focused mixture of chain-of-thought reasoning, math, coding, and general knowledge data. It is the compact, fast variant of the Inelly 4.5 family -- optimized for quick inference while retaining strong reasoning capabilities.

• Developed by: bry
• Base model: Qwen2.5-1.5B-Instruct
• Fine-tuning method: QLoRA (4-bit NF4, rank 16)
• Parameters: 1.54B (base) + ~3.1M trainable (LoRA adapters)
• License: Apache 2.0 (inherited from Qwen2.5)

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Intended Use

Inelly 4.5 Blaze is intended for:

• Chain-of-Thought reasoning – Step-by-step problem solving
• Math – Algebra, arithmetic, word problems
• Code generation – Python functions with clear logic
• Logical deduction – Syllogisms, puzzles, multi-step reasoning
• General knowledge Q&A – Science, everyday facts
• Quick prototyping – Fast inference on consumer hardware

Out of Scope

• Not intended for production deployment without further safety evaluation
• Less conversational polish than the 3B variant (Inelly 4.5)
• May struggle with very long or complex multi-step tasks

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Training Data

Inelly 4.5 Blaze was fine-tuned for 1 epoch on ~5,225 samples drawn from:

Dataset	Samples	Purpose
Bespoke-Stratos-35k	3,000	Chain-of-thought math & reasoning
OpenThoughts-114k	2,500	Code generation with reasoning
dolphin-r1	2,000	General reasoning (DeepSeek-R1 distill)

All samples were deduplicated and reasoning-weighted (2x oversample for CoT examples). Maximum sequence length: 512 tokens.

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Training Hyperparameters

Parameter	Value
Base model	Qwen2.5-1.5B-Instruct
Quantization	4-bit NF4 (bitsandbytes)
LoRA rank	16
LoRA alpha	32
LoRA dropout	0.05
Learning rate	2e-4
Batch size	8 (gradient accumulation)
Epochs	1
Max seq length	512
Optimizer	AdamW 8-bit
LR scheduler	cosine
Warmup ratio	0.05
Training time	~35 min
Hardware	RTX 2080 Ti (11GB VRAM)

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Model Architecture

Property	Value
Model type	Qwen2ForCausalLM
Hidden size	1,536
Layers	28
Attention heads	12
Head dim	128
Intermediate size	8,960
Vocab size	151,936
Context length	32,768
Total parameters	~1.54B
Trainable parameters	~3.1M (LoRA)

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Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("path/to/inelly-4.5-blaze", torch_dtype=torch.float16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("path/to/inelly-4.5-blaze")

messages = [{"role": "user", "content": "Solve for x: 3x + 7 = 22. Show all steps."}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

output = model.generate(**inputs, max_new_tokens=256, temperature=0.7, top_p=0.9)
response = tokenizer.decode(output[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(response)

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Performance

Informal GPU testing across 8 categories:

Category	Result
Chain-of-Thought reasoning	✅ Correct step-by-step logic
Math	✅ Accurate algebraic solutions
Code generation	✅ Clean Python with comments
Logic puzzles	✅ Sound deductive reasoning
General knowledge	✅ Accurate, clear explanations
Speed	✅ ~1-2s per response (faster than 3B/7B)

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Inelly 4.5 Family Comparison

Model	Size	Focus	Training Data
Inelly 4.5	3B	Conversation + CoT	5,700 samples (incl. politeness, conv)
Inelly 4.5 Blaze (this)	1.5B	Fast reasoning + CoT	5,225 samples (reasoning-focused)
Matrix 2	7B	Deep reasoning	5,225 samples (reasoning-focused)

When to use Blaze vs standard 4.5:

• Blaze – When you need fast reasoning, math, or coding help and don't need conversational polish
• 4.5 (3B) – When you want a friendly, polite conversationalist that can also reason

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Limitations

• Conversational ability: Less polished in casual chat compared to the 3B variant (no conversational fine-tuning data)
• Safety: Inherited from Qwen2.5 base; not specifically safety-tuned
• Context length: Fine-tuned on 512-token sequences
• Factual accuracy: May hallucinate in specialized domains

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Acknowledgments

• Qwen2.5 by Alibaba Cloud (base model)
• Bespoke Labs for Stratos dataset
• OpenThoughts team
• Cognitive Computations for dolphin-r1

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Citation

@misc{inelly45blaze,
  title = {Inelly 4.5 Blaze: A Compact Chain-of-Thought Reasoning Model},
  author = {Genue},
  year = {2026},
  note = {Fine-tuned from Qwen2.5-1.5B-Instruct using QLoRA},
}