paritok

paritok-4b-v1

README

License: apache-2.0

🔥 TL;DR

✨ Why Paritok?

🎨 Code-native. Trained end-to-end on 45K real coding-agent trajectories (file_read, bash_command, log_output...). Preserves function names, imports, paths, and error strings while compressing.
🚀 ~74% fewer tokens on typical workloads — up to 95% on heavy long-session traffic.
💰 Up to 95% off your input token bill on Claude Sonnet / GPT-4 (74% typical). Long-session teams save thousands per month.
🎯 86.5% of full-context solve quality retained on SWE-bench Verified — matching gpt-4.1-mini as compressor at less than half the token spend.
🪶 Small, fast, self-hostable. 4B LoRA adapter, bf16, single 24GB GPU. No SaaS, no lock-in, no per-token fee.
🔓 Fully open. Apache 2.0 weights, reproducible pipeline, real end-to-end benchmarks.

🚀 Quick Start

python
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

BASE_MODEL = "Qwen/Qwen3-4B-Instruct-2507"
ADAPTER    = "paritok/paritok-4b-v1"

tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
base = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL, torch_dtype=torch.bfloat16, device_map="auto"
)
model = PeftModel.from_pretrained(base, ADAPTER)
model.eval()

# Wrap your code as [SEG id=1 kind=file_read] ... [/SEG]
user_msg = "[SEG id=1 kind=file_read]\n<your code here>\n[/SEG]"

prompt = tokenizer.apply_chat_template(
    [{"role": "system", "content": "<full system prompt — see GitHub>"},
     {"role": "user",   "content": user_msg}],
    tokenize=False, add_generation_prompt=True,
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=1024, do_sample=False)
print(tokenizer.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

📎 Full runnable example + system prompt + reproduction pipeline: GitHub → Paritok-official/paritok-4b-v1

📊 SWE-bench Verified — Head-to-head

Table with columns: Context source, Quality retained ¹, Compression rate
Context source	Quality retained ¹	Compression rate
Uncompressed baseline	100.0%	100.0%
gpt-4.1-mini (compressor)	85.6%	50.2%
gpt-5 (compressor)	93.6%	61.9%
Paritok-4B-v1 ⭐	86.5%	25.7%

¹ Quality retained = compressor solve rate ÷ uncompressed baseline solve rate. Higher is better.

Paritok compresses ~2× harder than gpt-4.1-mini on the same task while keeping the same solve rate — the only open-source entry trained end-to-end on real coding-agent trajectories.

💰 Cost Impact (Claude Sonnet, $3/M input tokens)

Table with columns: Turn input size, Uncompressed input, With Paritok
Turn input size	Uncompressed input	With Paritok
Short (8K)	$0.024	$0.006
Typical (15K)	$0.045	$0.012
Long session (30K)	$0.090	$0.023

10-person team, 3-month project → save ~$10K on API bills.

Table with columns: Property, Value
Property	Value
Base model	Qwen/Qwen3-4B-Instruct-2507
Adapter type	LoRA, r=32, α=64, dropout=0.0
Target modules	`q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj`
Training steps	2000 (selected from a 5-checkpoint sweep on OOD holdout)
Training precision	bf16
Effective batch	32 (per_device=2 × grad_accum=16)

~6pp accuracy trade for 74% compression. Not free — buying context length with a small solve-rate drop. Bring your own uncompressed-fallback path for safety-critical turns.
Rare identifier loss (~40% preserved on hard OOD segments). Add a post-compression check that your target identifier is still in the compressed output before sending upstream.
English source code, Python-heavy training distribution. Non-English comments/strings and other-language codebases have not been benchmarked.

Recommended safeguards for production: format check, target-identifier presence check, and a session-level toggle to fall back to raw context on failure.

Paritok-4B-v2 — Next-generation training pipeline pushing compression to under 20% while closing the gap to uncompressed solve rate.
Frontier-scale backbones (10B+ parameters) for multi-day sessions with 100K+ token histories.
Multi-language expansion — TypeScript, Rust, Go, Java, C++, Kotlin.
Native integrations — mcp add paritok plugin for Claude Code and Cursor, plus a hosted inference endpoint.

📖 Citation

bibtex
@misc{paritok2026,
  author       = {Paritok Team},
  title        = {Paritok: Fine-tuned Compression for AI Coding-Agent Context},
  year         = {2026},
  publisher    = {GitHub},
  howpublished = {\url{https://github.com/Paritok-official/paritok-4b-v1}},
}

📄 License

Apache 2.0 — see LICENSE. Base model (Qwen3-4B-Instruct-2507) is under its own license; please review before commercial deployment.

Available on FriendliAI

Dedicated Endpoints

Run this model inference on single tenant GPU with unmatched speed and reliability at scale.

Learn more

Container

Run this model inference with full control and performance in your environment.

Learn more

Model Details

Model Provider

paritok

Model Tree

Base

Qwen/Qwen3-4B-Instruct-2507

Adapter

this model

Input Modalities

Text

Output Modalities