Base checkpoint: linear-moe-hub/Gated-Deltanet-1.3B
Architecture: Gated DeltaNet / linear recurrent attention
Base training data reported by the upstream model card: SlimPajama 100B-token sample
License inherited from upstream model card: Apache-2.0

Training Run

Local source path: runs/gdn1_32k_balanced_recovery_1b_bs10_ft/checkpoint-200
Tokenizer source: runs/gdn1_32k_balanced_recovery_1b_bs10_ft/final
Training mode: full fine-tuning, no LoRA/adapter
Hardware target: 8x NVIDIA H200
Sequence length: 32768
Approximate additional token budget: ~1B additional tokens
Manifest/config: configs/gdn1_memory_mix_32k_balanced_recovery.json

Intended Research Use

This checkpoint is intended for research on:

long-context associative recall
RULER/MQAR-style state tracking
recurrent-state contamination during long generation
Reference-State Reset with Rolling Replay, a GDN/RNN adaptation of the R-SWA idea

Usage

These checkpoints use the FLA Gated DeltaNet implementation. In the current Long-GDN environment, plain GatedDeltaNetForCausalLM.from_pretrained() can hit a Transformers 5.x tied-weight metadata issue. The robust path is to patch the FLA tied-weight metadata before loading.

Install/runtime requirements:

bash

pip install torch transformers safetensors huggingface_hub
# plus an FLA package/source tree that provides:
#   fla.models.gated_deltanet.GatedDeltaNetForCausalLM

CPU Example

python

import torch
from transformers import AutoTokenizer
from fla.models.gated_deltanet import GatedDeltaNetForCausalLM

repo_id = "LLM-OS-Models/gdn1-32k-balanced-recovery-1b-best"

# Transformers 5.x compatibility patch for the installed FLA class.
if isinstance(getattr(GatedDeltaNetForCausalLM, "_tied_weights_keys", None), list):
    GatedDeltaNetForCausalLM._tied_weights_keys = {
        "lm_head.weight": "model.embeddings.weight"
    }

tokenizer = AutoTokenizer.from_pretrained(repo_id, use_fast=True)
model = GatedDeltaNetForCausalLM.from_pretrained(
    repo_id,
    torch_dtype=torch.float32,
)
model.eval()

prompt = "A special magic number is 12345. What is the special magic number?"
inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
    output = model.generate(
        **inputs,
        max_new_tokens=32,
        do_sample=False,
    )
print(tokenizer.decode(output[0], skip_special_tokens=True))

Single-GPU bf16 Example

python

import torch
from transformers import AutoTokenizer
from fla.models.gated_deltanet import GatedDeltaNetForCausalLM

repo_id = "LLM-OS-Models/gdn1-32k-balanced-recovery-1b-best"

if isinstance(getattr(GatedDeltaNetForCausalLM, "_tied_weights_keys", None), list):
    GatedDeltaNetForCausalLM._tied_weights_keys = {
        "lm_head.weight": "model.embeddings.weight"
    }

tokenizer = AutoTokenizer.from_pretrained(repo_id, use_fast=True)
model = GatedDeltaNetForCausalLM.from_pretrained(
    repo_id,
    torch_dtype=torch.bfloat16,
).to("cuda")
model.eval()

prompt = "Reference facts:\n- key_alpha: value_123\n\nQuestion: key_alpha?\nAnswer:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
with torch.no_grad():
    output = model.generate(
        **inputs,
        max_new_tokens=32,
        do_sample=False,
    )
print(tokenizer.decode(output[0], skip_special_tokens=True))

Long-GDN Local Loader

The project repository includes a more defensive loader at scripts/gdn1_common.py::load_gdn1_causal_lm. It handles the compatibility patch and older public-checkpoint key conversion used in local experiments.

python

from pathlib import Path
import torch
from transformers import AutoTokenizer
from scripts.gdn1_common import load_gdn1_causal_lm

repo_or_local_path = Path("path/to/downloaded/checkpoint")
tokenizer = AutoTokenizer.from_pretrained(repo_or_local_path, use_fast=True)
model = load_gdn1_causal_lm(repo_or_local_path, torch_dtype=torch.bfloat16).to("cuda")

Known Results

Best current training checkpoint before marker-fix repair. MQAR smoke: 1K 0.1094, 2K 0.0781, 4K 0.1250, 8K 0.0938, 16K 0.1250, 32K 0.0625, 64K 0.2500. LongBench v2 full 503 likelihood matched public base at 0.196819 exact. Reference replay off/on improved 14.6K from 0.2812 to 0.6250 and 51.9K from 0.2500 to 0.5000 in local contamination evaluation.

Caveats

This is not a RULER 8K/16K long-retrieval win. RULER NIAH Single-2 smoke improved 4K slightly but still collapsed at 8K/16K. Use as a research checkpoint, not an instruction model.

Citation Context

Relevant background papers include Gated Delta Networks, Gated DeltaNet-2, Log-Linear Attention, and Unlimited OCR / R-SWA. This checkpoint does not implement a new architecture by itself; it is part of a checkpoint-preserving full fine-tuning and inference-control study.