deepseek-v4-mini-1B-from-flash

DeepSeek-V4 Mini (1B) — sliced from V4-Flash

A weight-transferred small replica of the DeepSeek-V4 architecture. Tensors were sliced (or, where shapes already matched, copied verbatim) from deepseek-ai/DeepSeek-V4-Flash into a smaller architecture matching kshitijthakkar/deepseek-v4-mini-1B-init.

This is not a randomly-initialized scaffold — most parameters carry real training signal from the source. It is intended as a warm-start for fine tuning at this size, not as a finished chat / instruct model. See the fine-tune recipe below.

Slice provenance

Each parameter in this model is one of three kinds:

Concretely:

• Layer pruning: source had 43 layers; we kept 24 via uniform stride: [0, 1, 3, 5, 7, 8, 10, 12, 14, 16, 17, 19, 21, 23, 25, 26, 28, 30, 32, 34, 35, 37, 39, 41]. The CSA/HCA/SW per-layer pattern is preserved.
• Expert pruning: source had 256 routed experts per layer; we kept the top-16 by gate.weight L2 norm (a cheap proxy for expert importance — replace with an activation-statistics-based pick if you have profiling data).
• Compressors / Indexer: marked INIT in this release (the per-block positional bias and gate require a non-trivial cross-dim mapping that the current slicer leaves at random; their parent attn KV/Q paths are sliced from source).
• MTP module: sliced from the source's first MTP step using the same per-tensor rules.
• Hash-routing tid2eid table: regenerated (top-k differs from source).

The exhaustive list of COPY (frozen-recommended) keys is in frozen_keys.json.

Quick start (Colab / local)

# 1) install + auth (private repo)
# !pip install -q "transformers>=4.50" huggingface_hub safetensors
from huggingface_hub import login, snapshot_download
login()

# 2) download repo (weights + tokenizer + modeling package)
local = snapshot_download(repo_id="kshitijthakkar/deepseek-v4-mini-1B-from-flash")

# 3) register the deepseek_v4 model_type with HF auto classes
import sys, os
sys.path.insert(0, os.path.join(local, "code"))
import deepseek_v4  # noqa: F401 — side-effect: AutoConfig / AutoModelForCausalLM registration

# 4) load via the standard Auto API
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained(local)
model = AutoModelForCausalLM.from_pretrained(local, torch_dtype=torch.bfloat16)
model.eval()

# 5) chat-templated forward pass — initial loss is much lower than the
# random-init scaffold, but the compressor/indexer paths are still random
# until you fine-tune (see below).
messages = [{"role": "user", "content": "Hello"}]
ids = tok.apply_chat_template(messages, return_tensors="pt",
                              add_generation_prompt=True, return_dict=True)
with torch.no_grad():
    print(model(input_ids=ids["input_ids"]).logits.shape)

Fine-tune recipe (warm-start)

Because most non-MoE-expert weights are already well-trained, freeze them and train only the lossy / random-init subset:

from training.warm_start import apply_warm_start_freeze
import os, json

frozen_path = os.path.join(local, "frozen_keys.json")
summary = apply_warm_start_freeze(model, frozen_path)
print(summary)
# -> trainable=Y M  frozen=X M  total=N M  train_ratio=...%

# Build optimizer over the trainable subset (configure_optimizers already
# respects requires_grad)
from training.optimizer import configure_optimizers
optimizer = configure_optimizers(
    model, learning_rate=2e-4, weight_decay=0.1,
    betas=(0.9, 0.95), device_type="cuda", optimizer_type="muon",
)

# ... standard training loop ...

Suggested starting hyperparameters:

Architecture summary

Limitations

• Compressor + Indexer modules are random-init (see Slice provenance above). The current slicer doesn't do the cross-dim mapping for those per-block tensors. Forward-pass logits are noisy through CSA/HCA layers until those are fine-tuned.
• Hash-table tid2eid regenerated — token routing for the first num_hash_layers differs from source.
• No FP4/FP8 weights — source is dequantized to BF16 at slice time, so this repo doesn't carry V4-Flash's quantization-aware training state.

Citation

@misc{deepseek_v4_2026,
  author = {DeepSeek-AI},
  title  = {DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence},
  year   = {2026},
  url    = {https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash}
}

paper PDF