frankenqwen3-8B-235B-dense-conversion-interleaved-untuned

frankenqwen3-8B-235B-dense-conversion-interleaved-untuned

• Base architecture: Qwen3 (dense)
• Sources:
  • Qwen/Qwen3-8B (base)
  • Qwen/Qwen3-235B-A22B-Instruct-2507 or Qwen/Qwen3-235B-A22-Thinking-2507 (MoE)
• Construction: Interleaved composite (passthrough-style), with MoE converted to dense first
• Intended use: Research and experimentation with layer-interleaved composites

Summary

• This model interleaves transformer blocks from Qwen3-8B with blocks derived from a Qwen3-235B-A22* MoE model converted to a dense format. The goal is to build a larger-than-8B dense model, while retaining tokenizer and base configuration from Qwen3-8B.
• MoE→Dense conversion is lossy; we use a safer approach than concatenating experts to reduce degradation.
• Final depth: 64 layers
• Hidden size: 4096
• FFN intermediate size: 12288
• Attention heads: 32 total, 8 KV heads (GQA)
• RoPE settings: inherited from Qwen3-8B base

NOTE: This model has not been fine-tuned or changed in any way after the merge. A recovery fine-tune would most likely increase performance significantly.

Model details

• Model type: qwen3
• Architectures: Qwen3ForCausalLM
• Tokenizer: Qwen3 tokenizer (copied from Qwen/Qwen3-8B)
• Tying: Embedding and lm_head tied (as in base)
• Positional encoding: RoPE (θ and scaling from base)
• Dtype of shards: bfloat16 (weights saved in bf16; can be loaded in fp16/bf16/fp32)

How it was built

1. MoE → Dense conversion

• Source: Qwen3-235B-A22* MoE.
• Method: Expert averaging (recommended: average or router_weighted; not concat).
• FFN orientation fixes:
  • up_proj, gate_proj: [intermediate, hidden]
  • down_proj: [hidden, intermediate]
• Attention (GQA) head-safe remap:
  • q_proj out = num_attention_heads × head_dim = hidden_size
  • k_proj/v_proj out = num_key_value_heads × head_dim
  • o_proj in = hidden_size
• Heads are remapped from source head counts to target (average when reducing heads, repeat when increasing), with head_dim adjusted by trunc/pad if needed.
• Non-MoE and non-router tensors preserved.

1. Composite interleaving

• Base: Qwen/Qwen3-8B (tokenizer, non-layer tensors, config baseline).
• Layers: Interleaved from base and converted MoE-dense into a new stack of 64 blocks.
• Strategy: Even distribution across the depth.
• Config: Copied from base; only num_hidden_layers updated to 64.

Intended use and limitations

• Intended for research on interleaved composite models and MoE→dense techniques.
• Not intended for production without evaluation and safety review.
• Known limitations:
  • MoE→dense is inherently lossy. Removing routers changes the function learned by the MoE experts.
  • Head remapping and FFN averaging are approximations.
  • Interleaving layers from different training distributions may degrade instruction-following or safety alignment.
  • Quality can often be improved with a brief recovery finetune (e.g., bf16, LR ~1e-5, a few thousand steps). This has not been performed on this model.

Evaluation

• No formal benchmarks provided with this release.
• Suggested sanity checks:
  • Perplexity on small corpora (should be in a reasonable range, not exploding).
  • Basic instruction-following and chat safety probes.
  • Compare generations against Qwen3-8B baseline.

Usage

Python (Transformers)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "snwy/frankenqwen3-8B-235B-dense-conversion-interleaved-untuned"
tok = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

# ...

CLI (text-generation-inference or transformers-cli)

• Any standard Qwen3-8B serving setup should work, as config/arch match the base.

Reproducibility (high-level)

• Convert MoE to dense (recommended method: average or router_weighted)
• Build composite interleaving with desired depth
• Validate load on meta device

Example commands

# 1) Convert MoE -> dense (average is safer than concat)
python moe_to_dense.py \
  --model_id Qwen/Qwen3-235B-A22B-Instruct-2507 \
  --target_model Qwen/Qwen3-8B \
  --output_path ./qwen3-235b-dense-avg \
  --method average \
  --low_memory

# 2) Build composite (example: 48 layers)
python moe_to_dense.py \
  --compose_interleaved \
  --base_model Qwen/Qwen3-8B \
  --moe_converted ./qwen3-235b-dense-avg \
  --composite_output_path ./qwen3-8b-plus-moe-48L \
  --final_layers 48 \
  --interleave_strategy even \
  --cast_dtype bfloat16 \
  --low_memory

# 3) Validate shapes/load
python moe_to_dense.py --validate_model ./qwen3-8b-plus-moe-48L

Safety, bias, and limitations

• May produce inaccurate or biased content inherited from source models.
• Not safe for deployment without additional alignment and filtering.
• Do not use for high-stakes or vulnerable-domain applications.

Versioning

• Version: v0.1 (first public composite)
• Changes from base:
  • Increased depth: 64 vs 32
  • Interleaved MoE-derived blocks after MoE→dense conversion
• RoPE and tokenizer inherited from base model

Licenses

• This composite inherits licenses from the source models. Please refer to:
  • Qwen/Qwen3-8B license
  • Qwen/Qwen3-235B-A22* license
• If redistributing, ensure compliance with both upstream licenses.

Acknowledgments

• Qwen team for Qwen3 models and tokenizer.
• Community tools: Hugging Face Transformers, safetensors, huggingface_hub.

Citation

• If you use this model, please cite the upstream Qwen3 models and this composite:

@misc{qwen3-8b-plus-moe-<FINAL_LAYERS>L,
  title  = {Qwen3 8B + MoE Interleaved Composite (64 Layers)},
  author = {snwy},
  year   = {2025},
  url    = {https://huggingface.co/snwy/frankenqwen3-8B-235B-dense-conversion-interleaved-untuned}
}

Notes for practitioners

• Smaller composites (e.g., 40–48 layers) tend to be more stable than very deep mixes without finetuning.
• If quality is marginal, try:
  • Reducing the fraction of MoE layers,
  • Router-weighted averaging during conversion,
  • Short recovery finetuning (bf16, LR ~1e-5).
• Ensure RoPE settings match the base across the entire composite (they do if you keep the base config).