"""
Create a tiny GigaChat3 model for testing .

GigaChat3 uses DeepseekV3Config (no text_config/vision_config sub-objects).
Key constraint: qk_head_dim == qk_nope_head_dim + qk_rope_head_dim
"""
import json
import os

import torch
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer

model_id = "ai-sage/GigaChat3-10B-A1.8B-bf16"
output_dir = "./tiny-gigachat3"

config = AutoConfig.from_pretrained(model_id)
config.num_hidden_layers = 2
config.num_attention_heads = 2
config.num_key_value_heads = 2
config.hidden_size = 32
config.intermediate_size = 64
config.moe_intermediate_size = 32
config.n_routed_experts = 4
config.n_shared_experts = 1
config.num_experts_per_tok = 2
config.kv_lora_rank = 8
config.q_lora_rank = None

# Attention head dims — MUST satisfy: qk_head_dim == qk_nope_head_dim + qk_rope_head_dim
config.qk_nope_head_dim = 4
config.qk_rope_head_dim = 2
config.qk_head_dim = 6   # 4 + 2
config.v_head_dim = 4
config.head_dim = config.qk_rope_head_dim  # used by RoPE

TINY_VOCAB = 32000
config.vocab_size = TINY_VOCAB

assert config.qk_head_dim == config.qk_nope_head_dim + config.qk_rope_head_dim

os.makedirs(output_dir, exist_ok=True)
model = AutoModelForCausalLM.from_config(config)
model.save_pretrained(output_dir)

tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.save_pretrained(output_dir)

tok_path = os.path.join(output_dir, "tokenizer.json")
with open(tok_path, encoding="utf-8") as f:
    tok_data = json.load(f)

if "model" in tok_data and "vocab" in tok_data["model"]:
    tok_data["model"]["vocab"] = {
        k: v for k, v in tok_data["model"]["vocab"].items() if v < TINY_VOCAB
    }
    tok_data["model"]["merges"] = []

if "added_tokens" in tok_data:
    tok_data["added_tokens"] = [t for t in tok_data["added_tokens"] if t["id"] < TINY_VOCAB]

with open(tok_path, "w", encoding="utf-8") as f:
    json.dump(tok_data, f, ensure_ascii=False)

# ── Smoke test ───────────────────────────────────────────────────────────────
tokens = tokenizer("Hello world", return_tensors="pt")
tokens.pop("token_type_ids", None)
with torch.no_grad():
    out = model(**tokens)

total_mb = sum(os.path.getsize(os.path.join(output_dir, fn)) for fn in os.listdir(output_dir)) / 1e6
print(f"shape={out.logits.shape}  params={sum(p.numel() for p in model.parameters())/1e6:.2f}M  size={total_mb:.1f} MB")

optimum-intel-internal-testing/tiny-random-gigachat3

Tiny model creation script:

optimum-intel-internal-testing/
tiny-random-gigachat3