INSTINCT-1-4B-Preview

INSTINCT-1-4B-Preview is a 4B-parameter reasoning model fine-tuned from Qwen3-4B-Thinking-2507 with a targeted focus on STEM and mathematical problem solving.
This model aims to improve consistency, correctness, and step-by-step reasoning quality within quantitative domains while maintaining the efficiency and accessibility of a small-scale model.

Model Overview

Attribute	Value
Base Model	Qwen3-4B-Thinking-2507
Parameters	~4B
Architecture	Decoder-only Transformer
Context Length	256K tokens (native)
Precision	FP16 (primary release)
License	Apache-2.0
Authors	Spestly

INSTINCT-1-4B-Preview inherits the long-context capabilities and reasoning-oriented training of its base model.
This finetune introduces domain-focused improvements in mathematical reasoning, symbolic manipulation, quantitative logic, and structured solution generation.

Intended Use

INSTINCT-1-4B-Preview is designed for:

Mathematical problem solving (algebra, calculus, discrete reasoning)
STEM reasoning tasks
Technical explanation and step-by-step derivations
Educational assistants and tutoring systems
Long-context scientific and technical document analysis
Coding tasks that involve quantitative or algorithmic reasoning

This release is a preview and is not intended for high-stakes decision-making or applications requiring verified formal correctness.

Training Summary

The model is refined using STEM-focused corpora emphasizing:

Multi-step reasoning sequences
Numerical accuracy and arithmetic consistency
Structured mathematical explanations
Technical correctness and logical coherence
STEM instruction-following alignment

The objective of the finetune is to reduce reasoning errors, improve solution structure, and increase stability across multi-step derivations.

More comprehensive training details will be released in the full version.

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_name = "Spestly/INSTINCT-1-4B-Preview"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = (
    "Solve the following problem step-by-step:\n"
    "Evaluate the integral ∫₀^∞ x^2 e^{-x} dx.\n"
    "Show all steps and provide the final answer in \\boxed{}."
)

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Recommended Inference Parameters

temperature: 0.4–0.7
top_p: 0.9–0.95
max_new_tokens: 256–800 depending on problem complexity

Serving (vLLM)

vllm serve Spestly/INSTINCT-1-4B-Preview \
    --tensor-parallel-size 1 \
    --dtype auto \
    --max-model-len 262144

Model Variants

Variant	Precision	Notes
INSTINCT-1-4B-Preview	FP16	Primary release
Additional variants	Pending	Quantized versions will be added

Limitations

The model may still generate incorrect or partially correct mathematical solutions.
Not suitable for domains requiring guaranteed formal correctness.
Extended chain-of-thought outputs may cause drift on extremely long derivations.
The dataset and training specifics are still being finalized in this preview stage.