GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
NadevA23
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
NadevA23
Model Tree
Input Modalities
Output Modalities
Supported Functionality
| Tier | Model | Parameters | Target Workload | Latency / Footprint |
|---|---|---|---|---|
| Edge / Workstation | Kronumos 2 Kairos | 7.6B | Rapid local bug remediation, offline laptops, CI/CD gates | <2.5s / 4-bit 5.5 GB VRAM |
| Edge / Sovereign | Kronumos 14B Kairos | 14.7B | Complex algebraic, cross-module AST repairs (sympy, sphinx) | <4.5s / 4-bit 9.2 GB VRAM |
| Titan / Enterprise | Kronumos Aion | 671B MoE | Deep multi-hop counterfactual reasoning & frontier SWE-bench | Enterprise Cluster / Cloud API |
Evaluated end-to-end on the official Princeton SWE-bench Verified benchmark (500 production instances across Django, Scikit-Learn, PyData Xarray, Sphinx, Sympy, etc.) using official Docker execution containers.
| Metric | Kronumos 2 Kairos | Industry Multi-Turn Baselines |
|---|---|---|
| Model Size | 7B Parameters | 70B - 405B / Frontier APIs |
| Execution Mode | Single-Pass Zero-Shot | Multi-Turn Agent Loop (50-100 Turns) |
| Avg Tokens / Task | 2,512 Tokens | 40,000 - 150,000 Tokens |
| Token Efficiency | 93.5% Reduction | Baseline (1.0x) |
| API Cost | $0.00 (Pure Local Weights) | 3.00−15.00 per issue |
| Verified Resolved Tasks | 8 Full Production Issues | - |
django__django-13569: Broken aggregation expression logic in database queries.django__django-13658: Management command argument parser collision.django__django-14855: Admin URL generation prefix regression.django__django-15104: Model custom key migration constraint hazard.django__django-16333: Many-to-many relationship foreign key mapping.pydata__xarray-4629: Multi-index coordinate slice dimension regression.scikit-learn__scikit-learn-10844: Pipeline estimators parameter validation fault.sphinx-doc__sphinx-8595: Python domain autodoc signature formatting error.Traditional LLM agents rely exclusively on multi-turn prompt loops, generating massive token overhead and hallucinating syntax formatting. Kronumos Kairos decouples cognition into two integrated computing cortices:
text
[Raw GitHub Issue Discussion] │ ▼ ┌──────────────────────────────────┐ │ Sub-Cortex IssueDeNoiser │ │ - Excises human chatter/quotes │ │ - Tags user reproduction code │ │ - Extracts Core Signal Triad │ └────────────────┬─────────────────┘ │ [Cleaned Technical Specification] │ ▼ ┌──────────────────────────────────┐ │ Procedural Cognitive Kernel │ ◄─── Procedural Seeds (<64 bytes) │ - BoundaryCondition Invariants │ (Zero-DB L1 Cache Execution) │ - DefensiveNullWrap / PopGuards │ └────────────────┬─────────────────┘ │ ┌────────────────────┴────────────────────┐ ▼ ▼┌───────────────────────┐ ┌───────────────────────┐│ CORTEX (Neural) │ │ SUBCORTEX (Deterministic)│ Qwen2.5-Coder-7B │ ◄─────► │ Tree-sitter AST Slicer││ - 5-Step CoT Reason │ Dual-Key│ Auto-Bracket & Indent ││ - Precise Code Hunk │ Consens.│ Merkle Causal Ledger │└───────────────────────┘ └───────────────────────┘This repository includes the precompiled native Linux x86_64 binary libtokenectomy_subcortex.so and Python C-ABI bridge tokenectomy_subcortex_rust.py.
python
from tokenectomy_subcortex_rust import RustSubCortex subcortex = RustSubCortex() # 1. Clean noisy issue descriptions (93.5% token reduction)clean_spec = subcortex.denoise_issue(raw_github_issue) # 2. Heal indentation drift and unbalanced brackets with sub-microsecond latency (5µs)healed_code = subcortex.heal_indentation(candidate_code, base_indent=4)python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_id = "NadevA23/Kronumos-Kairos-v2"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto") messages = [ {"role": "system", "content": "You are Kronumos Kairos, an expert autonomous program repair engine."}, {"role": "user", "content": "Fix the issue in the following function: def safe_divide(a, b): return a / b"}] inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)outputs = model.generate(inputs, max_new_tokens=512, do_sample=False)print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))For quantized local execution on consumer hardware, visit NadevA23/Kronumos-Kairos-v2-GGUF.
bibtex
@article{daffa2026kronumos2, author = {Muhammad Naufal Daffa}, title = {Kronumos 2 Kairos: Cost-Bounded Automated Program Repair via Dual-Brain Cybernetic Sub-Cortex on SWE-bench Verified}, journal = {Research Square}, year = {2026}, doi = {10.21203/rs.3.rs-11205335/v1}, url = {https://doi.org/10.21203/rs.3.rs-11205335/v1}}License: Apache 2.0
Maintained by: Tokenectomy Labs
[Raw GitHub Issue Discussion] │ ▼ ┌──────────────────────────────────┐ │ Sub-Cortex IssueDeNoiser │ │ - Excises human chatter/quotes │ │ - Tags user reproduction code │ │ - Extracts Core Signal Triad │ └────────────────┬─────────────────┘ │ [Cleaned Technical Specification] │ ▼ ┌──────────────────────────────────┐ │ Procedural Cognitive Kernel │ ◄─── Procedural Seeds (<64 bytes) │ - BoundaryCondition Invariants │ (Zero-DB L1 Cache Execution) │ - DefensiveNullWrap / PopGuards │ └────────────────┬─────────────────┘ │ ┌────────────────────┴────────────────────┐ ▼ ▼┌───────────────────────┐ ┌───────────────────────┐│ CORTEX (Neural) │ │ SUBCORTEX (Deterministic)│ Qwen2.5-Coder-7B │ ◄─────► │ Tree-sitter AST Slicer││ - 5-Step CoT Reason │ Dual-Key│ Auto-Bracket & Indent ││ - Precise Code Hunk │ Consens.│ Merkle Causal Ledger │└───────────────────────┘ └───────────────────────┘from tokenectomy_subcortex_rust import RustSubCortex subcortex = RustSubCortex() # 1. Clean noisy issue descriptions (93.5% token reduction)clean_spec = subcortex.denoise_issue(raw_github_issue) # 2. Heal indentation drift and unbalanced brackets with sub-microsecond latency (5µs)healed_code = subcortex.heal_indentation(candidate_code, base_indent=4)import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_id = "NadevA23/Kronumos-Kairos-v2"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto") messages = [ {"role": "system", "content": "You are Kronumos Kairos, an expert autonomous program repair engine."}, {"role": "user", "content": "Fix the issue in the following function: def safe_divide(a, b): return a / b"}] inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)outputs = model.generate(inputs, max_new_tokens=512, do_sample=False)print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))@article{daffa2026kronumos2, author = {Muhammad Naufal Daffa}, title = {Kronumos 2 Kairos: Cost-Bounded Automated Program Repair via Dual-Brain Cybernetic Sub-Cortex on SWE-bench Verified}, journal = {Research Square}, year = {2026}, doi = {10.21203/rs.3.rs-11205335/v1}, url = {https://doi.org/10.21203/rs.3.rs-11205335/v1}}