WithinUsAI/GPT2.5.2-high-reasoning-codex-0.4B

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ORIGINAL MODEL (Transformers) — README.md (FINAL)

Repo: WithinUsAI/GPT2.5.2-high-reasoning-codex-0.4B

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---

language:

• en

library_name: transformers pipeline_tag: text-generation

tags:

• gpt2
• causal-lm
• text-generation
• code
• coding
• reasoning
• instruct
• lightweight
• safetensors
• withinusai

license: other license_name: withinusai-custom-license license_link: LICENSE

base_model: openai-community/gpt2-medium base_model_relation: finetune

datasets:

• WithinUsAI/GPT-2-to-GPT-5-5k
• TeichAI/gpt-5.1-codex-max-1000x
• TeichAI/gpt-5.1-high-reasoning-1000x

metrics:

• pass@1
• accuracy
• exact_match

model-index: - name: WithinUsAI/GPT2.5.2-high-reasoning-codex-0.4B results: []

WithinUsAI/GPT2.5.2-high-reasoning-codex-0.4B

GPT-2 Medium enhanced toward GPT-5.2-style reasoning + codex behavior.
Small footprint. Built to ship working code. ⚡🧠

What “GPT2.5.2” means (project naming)

This model begins as GPT-2 Medium and is fine-tuned by WithIn Us AI with the goal of pushing behavior toward a GPT-5.2 “twin target” style: stronger stepwise reasoning, more reliable code generation, and improved instruction-following.

• GPT(2) = GPT-2 Medium base
• GPT(5.2) = target behavior style (reasoning + codex competence)
• GPT(2.5.2) = WithIn Us AI enhanced release line/version marker

Model details

• Model type: Decoder-only causal language model (GPT-2 family)
• Architecture: gpt2
• Size class: ~0.4B parameters (approx.)
• Base model: openai-community/gpt2-medium
• Base model relation: fine-tune
• Primary strengths: coding assistance, refactors, debugging, structured reasoning

Intended use

Recommended ✅

• Code generation & completion (Python-first; multi-language ok)
• Debugging: error → root cause → patch
• Refactoring: preserve behavior, improve clarity/perf
• Stepwise technical reasoning with constraints and edge cases

Not recommended 🚫

• High-stakes decisions (medical/legal/financial) without expert review
• Safety-critical systems without strict validation & monitoring

Quickstart (Transformers)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "WithinUsAI/GPT2.5.2-high-reasoning-codex-0.4B"

tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
    device_map="auto"
)

prompt = (
  "You are a senior software engineer.\n"
  "Task: Implement a robust JSONL reader in Python.\n"
  "First list edge cases, then write the implementation with comments.\n\n"
  "Answer:\n"
)

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
out = model.generate(
    **inputs,
    max_new_tokens=256,
    do_sample=True,
    temperature=0.7,
    top_p=0.95
)
print(tokenizer.decode(out[0], skip_special_tokens=True))