Quantization
- Tool: llm-compressor 0.14, GPTQ (actorder static, dampening 0.01, all 64 experts calibrated).
- Scheme: int4 symmetric weights, group size 128; activations bf16.
- int4: MoE experts (routed and shared), dense MLP of layer 0.
- bf16: MLA attention, router, embeddings,
lm_head, MTP layer.
Calibration data
512 samples, 897k tokens, max length 2048, chat template applied.
Benchmarks
lm-evaluation-harness, vLLM 0.25, RTX 3090, greedy, 500 samples per task.
Table with columns: Task, bf16, W4A16, Δ| Task | bf16 | W4A16 | Δ |
|---|
| gsm8k (5-shot) | 85.40 | 82.20 | -3.20 |
| ifeval (prompt strict) | 71.40 | 71.80 | +0.40 |
| mgsm_native_cot_ru (8-shot) | 73.20 | 68.00 | -5.20 |
| mgsm_native_cot_en (8-shot) | 86.40 | 85.60 | -0.80 |
| global_mmlu_full_ru | 64.43 | 63.25 | -1.18 |
| global_mmlu_full_en | 71.80 | 70.57 | -1.23 |
| arc_ru | 50.40 | 50.00 | -0.40 |
| arc_challenge | 60.20 | 59.20 | -1.00 |
| hellaswag_ru | 64.40 | 62.60 | -1.80 |
| hellaswag | 68.60 | 68.60 | 0.00 |
| belebele_rus_Cyrl | 80.80 | 78.00 | -2.80 |
| belebele_eng_Latn | 88.40 | 87.20 | -1.20 |
| Mean | | | -1.53 |
Usage
docker run --rm --gpus all --ipc=host -p 8000:8000 -v ~/.cache/huggingface:/root/.cache/huggingface \ -e VLLM_USE_DEEP_GEMM=0 vllm/vllm-openai:v0.25.0 your-hf-user/GigaChat3.1-10B-A1.8B-W4A16 \ --max-model-len 16384 --enable-auto-tool-choice --tool-call-parser gigachat3
License: MIT, as the base model.