feat: add Kimi-K2-Instruct-GGUF

src/emd/models/llms/__init__.py

@@ -6,6 +6,7 @@
     deepseek,
     baichuan,
     jina,
+    kimi,
     txgemma,
     medgemma
 )

src/emd/models/llms/kimi.py

@@ -0,0 +1,46 @@
+from .. import Model
+from ..engines import ktransformers_engine
+from ..services import (
+    sagemaker_service,
+    sagemaker_async_service,
+    ecs_service,
+    local_service
+)
+from ..frameworks import fastapi_framework
+from ..instances import (
+    g6e24xlarge_instance,
+    g6e48xlarge_instance,
+    local_instance
+)
+from emd.models.utils.constants import ModelType
+from ..model_series import KIMI_SERIES
+
+Model.register(
+    dict(
+        model_id="Kimi-K2-Instruct-Q4_K_M-GGUF",
+        supported_engines=[ktransformers_engine],
+        supported_instances=[
+            g6e24xlarge_instance,  # 4 GPUs, 96 vCPU, 768GB RAM - Minimum viable
+            g6e48xlarge_instance,  # 8 GPUs, 192 vCPU, 1536GB RAM - Optimal
+            local_instance         # Local deployment (600GB+ RAM required)
+        ],
+        supported_services=[
+            sagemaker_service,
+            sagemaker_async_service,
+            ecs_service,
+            local_service
+        ],
+        supported_frameworks=[
+            fastapi_framework
+        ],
+        allow_china_region=True,
+        huggingface_model_id="KVCache-ai/Kimi-K2-Instruct-GGUF",
+        huggingface_model_download_kwargs=dict(allow_patterns=["*Q4_K_M*"]),
+        require_huggingface_token=False,
+        application_scenario="Agentic AI, tool use, reasoning, coding, autonomous problem-solving",
+        description="Kimi K2 1T parameter MoE model with 32B activated parameters in GGUF Q4_K_M format. Optimized for KTransformers deployment with 600GB+ RAM requirement. Achieves 10-14 TPS performance.",
+        model_type=ModelType.LLM,
+        model_series=KIMI_SERIES,
+        need_prepare_model=False
+    )
+)

src/emd/models/model_series.py

@@ -151,3 +151,9 @@
     description="Baichuan Intelligent Technology.",
     reference_link="https://github.com/baichuan-inc"
 )
+
+KIMI_SERIES = ModelSeries(
+    model_series_name=ModelSeriesType.KIMI,
+    description="Kimi K2 is a state-of-the-art mixture-of-experts (MoE) language model with 32 billion activated parameters and 1 trillion total parameters. Trained with the Muon optimizer, Kimi K2 achieves exceptional performance across frontier knowledge, reasoning, and coding tasks while being meticulously optimized for agentic capabilities.",
+    reference_link="https://github.com/moonshotai/Kimi-K2"
+)

src/emd/models/utils/constants.py

@@ -234,3 +234,4 @@ class ModelSeriesType(ConstantBase):
     DEEPSEEK_REASONING_MODEL = "deepseek reasoning model"
     DEEPSEEK_v3 = "deepseek v3"
     BAICHUAN = "baichuan"
+    KIMI = "kimi"