Eyght/eyght-v-tx

Eyght V-TX (LoRA adapter)

A PEFT LoRA adapter for Qwen/Qwen2.5-7B-Instruct, fine-tuned by Eyght on the eyght-v-tx coding + reasoning dataset (software engineering, Git/GitHub, React/frontend, MCP/tool-use, C/C++, backend/API, advanced Python, step-by-step reasoning). This is the lightweight adapter (~154 MB) - load it on top of the base model.

Built, trained, and owned by Eyght. Free Hugging Face model repository.

Quick start

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base = "Qwen/Qwen2.5-7B-Instruct"
model = AutoModelForCausalLM.from_pretrained(base, torch_dtype="auto", device_map="auto")
model = PeftModel.from_pretrained(model, "Eyght/eyght-v-tx")
tok = AutoTokenizer.from_pretrained(base)
print(tok.decode(model.generate(**tok("Write a Python function to reverse a list.", return_tensors="pt").to(model.device), max_new_tokens=256)[0]))

Details

Base model	Qwen/Qwen2.5-7B-Instruct
Adapter	PEFT LoRA (r=16, q/k/v/o/gate/up/down)
Format	adapter_config.json + adapter_model.safetensors
Trainable params	40 M (0.5% of 7.66 B)
Dataset	eyght-v-tx (~20,000 coding + reasoning examples)
Framework	Unsloth (4-bit + LoRA SFT)
Hardware	1x NVIDIA RTX 4070 Ti (12 GB)

Merge to a standalone model (optional)

merged = model.merge_and_unload()
merged.save_pretrained("eyght-v-tx-merged")

Then quantize to GGUF (Q4_K_M ~4.7 GB) for Ollama.

License & attribution

• Adapter: Apache-2.0. Built, trained, and owned by Eyght.
• Respect the base model license when redistributing.

Built by Eyght with the local Eyght Veta studio.

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🧠 Project Janus — Dual-Loop Cognitive Architecture

This model is designed to operate within Project Janus, a dual-loop cognitive architecture that goes beyond simple prompt-response:

The Core Cognitive Loop

Perceive → Working Memory → Internal Critic → Action → Consolidation

Phase	Component	Function
1. Perceive	Input mapping	Raw data → dense vector space
2. Working Memory	Dynamic scratchpad	Active goal, hypotheses, constraints
3. Internal Critic	Value function	Evaluates outputs before execution (quality + safety)
4. Action	Response delivery	Approved output delivered to the user
5. Consolidation	Durable memory	Successful strategies stored for future retrieval

Three-Tier Memory

• Episodic Store — append-only memory of past tasks and successes (retrieval-augmented)
• Procedural Skill Library — reusable reasoning patterns compiled into adapters
• Core Value Axioms — frozen safety rules preventing drift as the agent learns

Self-correction + Memory + Safety

The Internal Critic evaluates every draft response against core value axioms before delivery. This enables self-correction, memory-augmented reasoning (RAG), and continuous learning without catastrophic forgetting.

Built by Eyght. Project Janus — a cognitive architecture, not just a model.