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kshitijthakkar/deepseek-v4-mini-3B-init
deepseek-v4-mini-3B-init is a text generation model from kshitijthakkar. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as mit.
Faithful small-scale (~3.2B total / ~1.10B activated per token) replica of the DeepSeek-V4 architecture, sized to be trainable on rented GPUs and to map cleanly onto the full-scale V4-Flash dimensions for weight slicing.
Downloads · 30 days
182
46% of all-time downloads
All-time downloads
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3.2B
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.safetensors6.3 GB · 100%
How the weights are stored.
BF163.2B · 100%
From the Hugging Face model README
Faithful small-scale (~3.2B total / ~1.10B activated per token) replica of the DeepSeek-V4 architecture, sized to be trainable on rented GPUs and to map cleanly onto the full-scale V4-Flash dimensions for weight slicing.
This is a randomly-initialized scaffold — generates noise. Its purpose:
| Value | |
|---|---|
| hidden_size | 1536 |
| num_hidden_layers | 28 |
| num_attention_heads | 24 |
| num_key_value_heads | 1 (MQA) |
| head_dim | 64 |
| q_lora_rank / o_lora_rank | 512 / 512 |
| qk_rope_head_dim | 32 |
| o_groups | 4 |
| n_routed_experts | 24 |
| n_shared_experts | 1 |
| num_experts_per_tok | 4 |
| num_hash_layers | 2 |
| moe_intermediate_size | 768 |
| compress_ratios | [0, 0, 4, 112, 4, 112, 4, 112, 4, 112, 4, 112, 4, 112, 4, 112, 4, 112, 4, 112, 4, 112, 4, 112, 4, 112, 4, 0] |
| index_topk / heads / head_dim | 192 / 16 / 96 |
| sliding_window | 64 |
| max_position_embeddings | 1,048,576 (YaRN factor=16) |
| vocab_size | 129280 (real V4-Flash tokenizer) |
| num_nextn_predict_layers | 1 (V3-style MTP) |
| hc_mult (n_hc) | 4 |
| Storage dtype | bfloat16 |
from huggingface_hub import login, snapshot_download
login() # private repo
local = snapshot_download(repo_id="kshitijthakkar/deepseek-v4-mini-3B-init")
import sys, os
sys.path.insert(0, os.path.join(local, "code"))
import deepseek_v4 # registers DeepseekV4{Config,ForCausalLM} with HF auto classes
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained(local)
model = AutoModelForCausalLM.from_pretrained(local, torch_dtype=torch.bfloat16)
model.eval()
ids = tok.apply_chat_template(
[{"role": "user", "content": "Hello"}],
return_tensors="pt", add_generation_prompt=True, return_dict=True,
)
with torch.no_grad():
out = model(input_ids=ids["input_ids"])
print(out.logits.shape)
mHC (Sinkhorn-Knopp) · CSA + Lightning Indexer · HCA · pure sliding-window ·
Shared-KV MQA + grouped output projection (per-group wo_a) · partial RoPE +
output -i rotation · attention sink · DeepseekMoE with sqrt(softplus)
routing · hash-routed early layers · clamped SwiGLU · MTP head · YaRN.
Every component is bit-equivalent in math to the official inference/model.py
kernel.py:hc_split_sinkhorn (FP4/FP8 quantization and Hadamard rotation
are skipped — those are inference optimizations, not architecture).@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}
}