commit 556f22482379146df9d7c158b331237d338b9781
Author: ModelHub XC <noreply@modelhub.org.cn>
Date:   Sat Oct 10 14:55:15 2026 +0800

    初始化项目，由ModelHub XC社区提供模型
    
    Model: ai-sage/GigaChat3-10B-A1.8B-base
    Source: Original Platform

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diff --git a/LICENSE.txt b/LICENSE.txt
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+MIT License
+
+Copyright (c) 2025 Salute Developers
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/README.md b/README.md
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+---
+license: mit
+language:
+- ru
+- en
+pipeline_tag: text-generation
+tags:
+- moe
+---
+# GigaChat3-10B-A1.8B-base
+
+Представляем `GigaChat3-10B-A1.8B-base` — базовую (pretrain) модель семейства GigaChat. Модель основана на архитектуре Mixture-of-Experts (MoE) с 10B общих и 1.8B активных параметров.
+Архитектура включает **Multi-head Latent Attention (MLA)** и **Multi-Token Prediction (MTP)**, за счет чего модель оптимизирована для высокой пропускной способности (throughput) при инференсе.
+Больше подробностей [в хабр статье](https://habr.com/en/companies/sberdevices/articles/968904/).
+
+## Важное замечание: Base vs. Instruct
+
+Данная карточка описывает **базовую** (pretrain) модель.
+Для диалоговых задач и выполнения инструкций, пожалуйста, используйте нашу [**instruct**-версию](https://huggingface.co/ai-sage/GigaChat3-10B-A1.8B).
+
+## Архитектура модели
+
+`GigaChat3-10B-A1.8B-base` использует кастомную MoE-архитектуру:
+
+### Multi-head Latent Attention (MLA)
+
+Вместо стандартного Multi-head Attention модель использует MLA. MLA обеспечивает эффективный инференс за счет сжатия Key-Value (KV) кэша в латентный вектор, что значительно снижает требования к памяти и ускоряет обработку.
+
+### Multi-Token Prediction (MTP)
+
+Модель обучена с использованием задачи Multi-Token Prediction (MTP). Это позволяет модели предсказывать несколько токенов за один проход, что ускоряет генерацию до 40% с помощью техник спекулятивной/параллельной генерации.
+
+## Данные для обучения
+
+Модель обучена на 20Т токенов.
+Мы добавили 10 языков — от китайского и арабского до узбекского и казахского, а также расширили набор источников: книги, академические данные, датасеты по коду и математике. Все данные проходят дедупликацию, языковую фильтрацию и автоматические проверки качества при помощи эвристик и классификаторов.
+Ключевой вклад в качество внесла синтетика: мы сгенерировали около 5,5 триллионов токенов синтетических данных. В корпус входят вопросы-ответы к текстам, цепочки reverse-prompt для структурирования данных, LLM-заметки с комментариями от модели внутри текстов, миллионы синтетических задач с решениями по математике и олимпиадному программированию (с синтетическими тестами) на основе PromptCot.
+
+## Инференс
+
+Одно из ключевых преимуществ `GigaChat3-10B-A1.8B-base` — скорость инференса. Модель (особенно в режиме MTP) демонстрирует пропускную способность, сопоставимую с пропускной способностью значительно меньших dense‑моделей.
+Измерения проводились с помощью vLLM v0.11.0, на типе bfloat16 c  `batch_size=1`.
+[Ссылка на код.](https://gist.github.com/ajpqs/ce941aa6f0f48ef36a65cb87a2a1d726)
+
+|**Модель**|**request_throughput**|**output_throughput**|**total_token_throughput**|**mean_ttft_ms**|
+|---|---|---|---|---|
+|`Qwen3-1.7B`|1.689|357.308|726.093|11.824|
+|**`mtp-GigaChat3-10B-A1.8B-base`**|**1.533**|**333.620**|**678.894**|**26.345**|
+|**`GigaChat3-10B-A1.8B-base`**|**1.077**|**234.363**|**476.912**|**31.053**|
+|`Qwen3-4B`|0.978|206.849|420.341|14.947|
+|`Qwen3-8B`|0.664|140.432|285.375|16.663|
+|`YandexGPT-5-Lite-8B-pretrain`|0.641|147.305|300.269|16.711|
+
+
+## Бенчмарки
+
+Хотя модель имеет 10 миллиардов параметров, её прямые аналоги — модели размером 3–4 миллиарда параметров. Однако благодаря высокой скорости генерации мы также сравниваем её с ещё более компактными моделями.
+
+![image](https://cdn-uploads.huggingface.co/production/uploads/66356a710508bbbb61184fd2/3lxW7o5sJtzcIRpVMg15c.png)
+
+
+## Как проверить метрики модели
+
+```shell
+# lm-eval[api]==0.4.9.1
+# sglang[all]==0.5.5
+# или 
+# vllm==0.11.2
+
+export HF_ALLOW_CODE_EVAL=1
+
+# sglang server up
+
+# 10B
+python -m sglang.launch_server --model-path <path_to_model> --host 127.0.0.1 --port 30000 --dtype auto --mem-fraction-static 0.88 --trust-remote-code --allow-auto-truncate --speculative-algorithm EAGLE --speculative-num-steps 1 --speculative-eagle-topk 1 --speculative-num-draft-tokens 2
+
+# mmlu pro check
+python -m lm_eval --model sglang-generate --output_path <path_to_model> --batch_size 16 --model_args base_url=http://127.0.0.1:30000/generate,num_concurrent=16,tokenized_requests=True,max_length=131072,tokenizer=<path_to_model> --trust_remote_code --confirm_run_unsafe_code --num_fewshot 5 --tasks mmlu_pro
+
+```
+
+## Пример использования (Quickstart)
+
+### 1.  `transformers`
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
+
+model_name = "ai-sage/GigaChat3-10B-A1.8B-base"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
+model.generation_config = GenerationConfig.from_pretrained(model_name)
+
+messages = (
+    "Ниже я написал подробное доказательство теоремы о неподвижной точке:"
+)
+inputs = tokenizer(messages, return_tensors="pt").to(model.device)
+outputs = model.generate(inputs.input_ids, attention_mask=inputs.attention_mask)
+
+result = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=False)
+print(result)
+```
+
+### 2. `vLLM`
+
+Запуск сервера
+```shell
+vllm serve ai-sage/GigaChat3-10B-A1.8B-base \
+  --dtype "auto" \
+  --speculative-config '{"method": "mtp", "num_speculative_tokens": 1, "disable_padded_drafter_batch": false}'
+```
+
+Пример запроса
+```shell
+curl http://localhost:8000/v1/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "ai-sage/GigaChat3-10B-A1.8B-base",
+    "prompt": "Ниже я написал подробное доказательство теоремы о неподвижной точке:",
+    "max_tokens": 400,
+    "temperature": 0
+  }'
+```
+
+### 3. `SGLang`
+
+Запуск сервера
+```shell
+python -m sglang.launch_server \
+  --model-path ai-sage/GigaChat3-10B-A1.8B-base \
+  --host 0.0.0.0 \
+  --port 30000 \
+  --dtype auto \
+  --mem-fraction-static 0.88 \
+  --speculative-algorithm EAGLE \
+  --speculative-num-steps 1 \
+  --speculative-eagle-topk 1 \
+  --speculative-num-draft-tokens 2
+```
+
+Пример запроса
+```shell
+curl http://localhost:30000/v1/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "ai-sage/GigaChat3-10B-A1.8B-base",
+    "prompt": "Ниже я написал подробное доказательство теоремы о неподвижной точке:",
+    "max_tokens": 400,
+    "temperature": 0
+  }'
+```
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diff --git a/config.json b/config.json
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+{
+  "vocab_size": 128256,
+  "max_position_embeddings": 262144,
+  "hidden_size": 1536,
+  "intermediate_size": 8960,
+  "moe_intermediate_size": 1280,
+  "num_hidden_layers": 26,
+  "num_nextn_predict_layers": 1,
+  "num_attention_heads": 32,
+  "n_shared_experts": 1,
+  "n_routed_experts": 64,
+  "ep_size": 1,
+  "routed_scaling_factor": 1,
+  "kv_lora_rank": 512,
+  "q_lora_rank": null,
+  "qk_rope_head_dim": 64,
+  "v_head_dim": 192,
+  "qk_nope_head_dim": 128,
+  "topk_method": "noaux_tc",
+  "n_group": 1,
+  "topk_group": 1,
+  "num_experts_per_tok": 4,
+  "moe_layer_freq": 1,
+  "first_k_dense_replace": 1,
+  "norm_topk_prob": true,
+  "scoring_func": "sigmoid",
+  "num_key_value_heads": 32,
+  "hidden_act": "silu",
+  "initializer_range": 0.006,
+  "rms_norm_eps": 1e-06,
+  "use_cache": true,
+  "rope_theta": 100000,
+  "rope_scaling": {
+    "beta_fast": 32,
+    "beta_slow": 1,
+    "factor": 64.0,
+    "mscale": 1.0,
+    "mscale_all_dim": 1.0,
+    "original_max_position_embeddings": 4096,
+    "type": "yarn"
+  },
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "tie_word_embeddings": false,
+  "architectures": [
+    "DeepseekV3ForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.53.2",
+  "model_type": "deepseek_v3",
+  "torch_dtype": "bfloat16"
+}
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diff --git a/configuration.json b/configuration.json
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+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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diff --git a/generation_config.json b/generation_config.json
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+{
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 2,
+  "transformers_version": "4.53.2"
+}
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+  "extra_special_tokens": {},
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+  "tokenizer_class": "PreTrainedTokenizer",
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+}