Add glm 4.7

README.md

@@ -70,9 +70,10 @@ rest of the cells run as-is.
 
 | Tutorial | Summary | Difficulty | Framework | Launch |
 |---|---|---|---|---|
-| [`000_rl_basics`](tutorials/rl/000_rl_basics/000_rl_basics.ipynb) | Qwen3-4B haiku evaluation with verifiable rewards — serve, evaluate, train, compare | Beginner | `slime` | <a href="https://modal.com/notebooks/new/https://github.com/modal-projects/training-gym/blob/devin/1778791977-positive-rewards-001-sandboxes/tutorials/rl/000_rl_basics/000_rl_basics.ipynb" target="_blank" rel="nofollow noopener noreferrer"><img src="https://modal-cdn.com/open-in-modal.svg" alt="Open in Modal"></a> |
-| [`001_sandboxes`](tutorials/rl/001_sandboxes/001_sandboxes.ipynb) | Code RL with Harbor hello-world and sandboxed verification | Intermediate | `slime` | <a href="https://modal.com/notebooks/new/https://github.com/modal-projects/training-gym/blob/devin/1778791977-positive-rewards-001-sandboxes/tutorials/rl/001_sandboxes/001_sandboxes.ipynb" target="_blank" rel="nofollow noopener noreferrer"><img src="https://modal-cdn.com/open-in-modal.svg" alt="Open in Modal"></a> |
-| [`002_multiturn`](tutorials/rl/002_multiturn/002_multiturn.ipynb) | Multi-turn number-guessing RL with custom generate and reward functions | Intermediate | `slime` | <a href="https://modal.com/notebooks/new/https://github.com/modal-projects/training-gym/blob/devin/1778791977-positive-rewards-001-sandboxes/tutorials/rl/002_multiturn/002_multiturn.ipynb" target="_blank" rel="nofollow noopener noreferrer"><img src="https://modal-cdn.com/open-in-modal.svg" alt="Open in Modal"></a> |
+| [`000_rl_basics`](tutorials/rl/000_rl_basics/000_rl_basics.ipynb) | Qwen3-4B haiku evaluation with verifiable rewards — serve, evaluate, train, compare | Beginner | `slime` | <a href="https://modal.com/notebooks/new/https://github.com/modal-projects/training-gym/blob/main/tutorials/rl/000_rl_basics/000_rl_basics.ipynb" target="_blank" rel="nofollow noopener noreferrer"><img src="https://modal-cdn.com/open-in-modal.svg" alt="Open in Modal"></a> |
+| [`001_sandboxes`](tutorials/rl/001_sandboxes/001_sandboxes.ipynb) | Code RL with Harbor hello-world and sandboxed verification | Intermediate | `slime` | <a href="https://modal.com/notebooks/new/https://github.com/modal-projects/training-gym/blob/main/tutorials/rl/001_sandboxes/001_sandboxes.ipynb" target="_blank" rel="nofollow noopener noreferrer"><img src="https://modal-cdn.com/open-in-modal.svg" alt="Open in Modal"></a> |
+| [`002_multiturn`](tutorials/rl/002_multiturn/002_multiturn.ipynb) | Multi-turn number-guessing RL with custom generate and reward functions | Intermediate | `slime` | <a href="https://modal.com/notebooks/new/https://github.com/modal-projects/training-gym/blob/main/tutorials/rl/002_multiturn/002_multiturn.ipynb" target="_blank" rel="nofollow noopener noreferrer"><img src="https://modal-cdn.com/open-in-modal.svg" alt="Open in Modal"></a> |
+| [`003_glm_gsm8k`](tutorials/rl/003_glm_gsm8k/003_glm_gsm8k.ipynb) | GLM-4.7 (355B MoE) on GSM8K math — serve, evaluate, GRPO-train, compare | Advanced | `slime` | <a href="https://modal.com/notebooks/new/https://github.com/modal-projects/training-gym/blob/main/tutorials/rl/003_glm_gsm8k/003_glm_gsm8k.ipynb" target="_blank" rel="nofollow noopener noreferrer"><img src="https://modal-cdn.com/open-in-modal.svg" alt="Open in Modal"></a> |
 <!-- END TUTORIAL TABLE -->
 
 See [`tutorials/README.md`](tutorials/README.md) for how to run the `.py`

modal_training_gym/__init__.py

@@ -10,6 +10,8 @@
     "EvalConfigDurable": ("modal_training_gym.common.eval", "EvalConfigDurable"),
     "EvalResult": ("modal_training_gym.common.eval", "EvalResult"),
     "EvalRowResult": ("modal_training_gym.common.eval", "EvalRowResult"),
+    "GLM_4_7": ("modal_training_gym.common.models", "GLM_4_7"),
+    "GLM_4_7_Flash": ("modal_training_gym.common.models", "GLM_4_7_Flash"),
     "HFModelConfiguration": (
         "modal_training_gym.common.models",
         "HFModelConfiguration",
@@ -60,6 +62,8 @@
     "ModelConfig",
     "ModelDeployment",
     "MultiTurn",
+    "GLM_4_7",
+    "GLM_4_7_Flash",
     "Qwen3_0_6B",
     "Qwen3_1_7B",
     "Qwen3_4B",

modal_training_gym/common/models/__init__.py

@@ -3,6 +3,8 @@
     ModelArchitecture,
     ModelConfig,
 )
+from .glm_4_7 import GLM_4_7
+from .glm_4_7_flash import GLM_4_7_Flash
 from .qwen3_0_6b import Qwen3_0_6B
 from .qwen3_1_7b import Qwen3_1_7B
 from .qwen3_4b import Qwen3_4B
@@ -12,6 +14,8 @@
 from .qwen3_32b import Qwen3_32B
 
 __all__ = [
+    "GLM_4_7",
+    "GLM_4_7_Flash",
     "HFModelConfiguration",
     "ModelArchitecture",
     "ModelConfig",

modal_training_gym/common/models/base.py

@@ -75,6 +75,13 @@ class ModelArchitecture:
     use_rotary_position_embeddings: bool = True
     rotary_base: int = 10000
 
+    # MoE (Mixture of Experts)
+    num_experts: int = 0
+    moe_router_topk: int = 0
+    moe_ffn_hidden_size: int = 0
+    num_shared_experts: int = 0
+    first_k_dense_replace: int = 0
+
     def to_megatron_args(self) -> list[str]:
         """Generate Megatron-LM CLI flags from this architecture spec."""
         args: list[str] = []
@@ -111,6 +118,16 @@ def to_megatron_args(self) -> list[str]:
             args += ["--position-embedding-type", "rope"]
             if self.rotary_base != 10000:
                 args += ["--rotary-base", str(self.rotary_base)]
+        if self.num_experts:
+            args += ["--num-experts", str(self.num_experts)]
+        if self.moe_router_topk:
+            args += ["--moe-router-topk", str(self.moe_router_topk)]
+        if self.moe_ffn_hidden_size:
+            args += ["--moe-ffn-hidden-size", str(self.moe_ffn_hidden_size)]
+        if self.num_shared_experts:
+            args += ["--num-shared-experts", str(self.num_shared_experts)]
+        if self.first_k_dense_replace:
+            args += ["--first-k-dense-replace", str(self.first_k_dense_replace)]
         return args
 
 

modal_training_gym/common/models/glm_4_7.py

@@ -0,0 +1,38 @@
+"""GLM-4.7 (355B-A32B MoE) model spec."""
+
+from .base import HFModelConfiguration, ModelArchitecture
+
+
+class GLM_4_7(HFModelConfiguration):
+    """GLM-4.7 (355B total, 32B active) MoE from Zhipu AI.
+
+    160 routed experts with top-8 routing plus 1 shared expert.
+    First 3 layers are dense; remaining 89 are MoE.
+    Uses GQA (96 Q heads, 8 KV heads) with partial RoPE.
+    Downloads from ``zai-org/GLM-4.7`` on HuggingFace.
+    """
+
+    model_name = "zai-org/GLM-4.7"
+    architecture = ModelArchitecture(
+        num_layers=92,
+        hidden_size=5120,
+        ffn_hidden_size=12288,
+        num_attention_heads=96,
+        group_query_attention=True,
+        num_query_groups=8,
+        kv_channels=128,
+        vocab_size=151552,
+        normalization="RMSNorm",
+        norm_epsilon=1e-5,
+        swiglu=True,
+        disable_bias_linear=False,
+        qk_layernorm=True,
+        untie_embeddings_and_output_weights=True,
+        use_rotary_position_embeddings=True,
+        rotary_base=1000000,
+        num_experts=160,
+        moe_router_topk=8,
+        moe_ffn_hidden_size=1536,
+        num_shared_experts=1,
+        first_k_dense_replace=3,
+    )

modal_training_gym/common/models/glm_4_7_flash.py

@@ -0,0 +1,36 @@
+"""GLM-4.7-Flash (30B-A3B MoE) model spec."""
+
+from .base import HFModelConfiguration, ModelArchitecture
+
+
+class GLM_4_7_Flash(HFModelConfiguration):
+    """GLM-4.7-Flash (30B total, 3B active) MoE from Zhipu AI.
+
+    64 routed experts with top-4 routing plus 1 shared expert.
+    Uses Multi-head Latent Attention (MLA) and multi-token prediction.
+    Downloads from ``zai-org/GLM-4.7-Flash`` on HuggingFace.
+    """
+
+    model_name = "zai-org/GLM-4.7-Flash"
+    architecture = ModelArchitecture(
+        num_layers=47,
+        hidden_size=2048,
+        ffn_hidden_size=10240,
+        num_attention_heads=20,
+        group_query_attention=False,
+        num_query_groups=20,
+        kv_channels=128,
+        vocab_size=154880,
+        normalization="RMSNorm",
+        norm_epsilon=1e-5,
+        swiglu=True,
+        disable_bias_linear=True,
+        qk_layernorm=True,
+        use_rotary_position_embeddings=True,
+        rotary_base=1000000,
+        num_experts=64,
+        moe_router_topk=4,
+        moe_ffn_hidden_size=1536,
+        num_shared_experts=1,
+        first_k_dense_replace=1,
+    )

modal_training_gym/deploy_recipes/sglang_recipe/__init__.py

@@ -1,4 +1,10 @@
 from modal_training_gym.deploy_recipes.sglang_recipe.recipe import SglangRecipe
+from modal_training_gym.deploy_recipes.sglang_recipe.glm_4_7 import (
+    GLM_4_7_SglangRecipe,
+)
+from modal_training_gym.deploy_recipes.sglang_recipe.glm_4_7_flash import (
+    GLM_4_7_Flash_SglangRecipe,
+)
 from modal_training_gym.deploy_recipes.sglang_recipe.qwen3_0_6b import (
     Qwen3_0_6b_SglangRecipe,
 )
@@ -22,6 +28,8 @@
 )
 
 __all__ = [
+    "GLM_4_7_SglangRecipe",
+    "GLM_4_7_Flash_SglangRecipe",
     "SglangRecipe",
     "Qwen3_0_6b_SglangRecipe",
     "Qwen3_1_7b_SglangRecipe",

modal_training_gym/deploy_recipes/sglang_recipe/glm_4_7.py

@@ -0,0 +1,26 @@
+from dataclasses import dataclass
+
+from modal_training_gym.deploy_recipes.sglang_recipe.recipe import SglangRecipe
+
+_GLM_4_7_DEFAULTS = {
+    "gpu": "H100",
+    "tp": 8,
+    "context_length": 32768,
+    "mem_fraction_static": 0.80,
+    "chunked_prefill_size": 8192,
+    "max_running_requests": 16,
+    "extra_server_args": {"--trust-remote-code": ""},
+}
+
+
+_SGLANG_DEFAULTS = SglangRecipe()
+
+
+@dataclass
+class GLM_4_7_SglangRecipe(SglangRecipe):
+    """GLM-4.7 (355B) on 8×H100 — tensor-parallel MoE serving."""
+
+    def __post_init__(self) -> None:
+        for key, val in _GLM_4_7_DEFAULTS.items():
+            if getattr(self, key) == getattr(_SGLANG_DEFAULTS, key):
+                object.__setattr__(self, key, val)

modal_training_gym/deploy_recipes/sglang_recipe/glm_4_7_flash.py

@@ -0,0 +1,27 @@
+from dataclasses import dataclass
+
+from modal_training_gym.deploy_recipes.sglang_recipe.recipe import SglangRecipe
+
+_GLM_4_7_FLASH_DEFAULTS = {
+    "gpu": "H100",
+    "tp": 1,
+    "dp": 8,
+    "context_length": 32768,
+    "mem_fraction_static": 0.80,
+    "chunked_prefill_size": 8192,
+    "max_running_requests": 16,
+    "extra_server_args": {"--trust-remote-code": ""},
+}
+
+
+_SGLANG_DEFAULTS = SglangRecipe()
+
+
+@dataclass
+class GLM_4_7_Flash_SglangRecipe(SglangRecipe):
+    """GLM-4.7-Flash on 8×H100 — DP-attention MoE serving."""
+
+    def __post_init__(self) -> None:
+        for key, val in _GLM_4_7_FLASH_DEFAULTS.items():
+            if getattr(self, key) == getattr(_SGLANG_DEFAULTS, key):
+                object.__setattr__(self, key, val)

modal_training_gym/deploy_recipes/vllm_recipe/__init__.py

@@ -1,4 +1,10 @@
 from modal_training_gym.deploy_recipes.vllm_recipe.recipe import VllmRecipe
+from modal_training_gym.deploy_recipes.vllm_recipe.glm_4_7 import (
+    GLM_4_7_VllmRecipe,
+)
+from modal_training_gym.deploy_recipes.vllm_recipe.glm_4_7_flash import (
+    GLM_4_7_Flash_VllmRecipe,
+)
 from modal_training_gym.deploy_recipes.vllm_recipe.qwen3_0_6b import (
     Qwen3_0_6b_VllmRecipe,
 )
@@ -12,6 +18,8 @@
 from modal_training_gym.deploy_recipes.vllm_recipe.qwen3_32b import Qwen3_32b_VllmRecipe
 
 __all__ = [
+    "GLM_4_7_VllmRecipe",
+    "GLM_4_7_Flash_VllmRecipe",
     "VllmRecipe",
     "Qwen3_0_6b_VllmRecipe",
     "Qwen3_1_7b_VllmRecipe",

modal_training_gym/deploy_recipes/vllm_recipe/glm_4_7.py

@@ -0,0 +1,21 @@
+from dataclasses import dataclass
+
+from modal_training_gym.deploy_recipes.vllm_recipe.recipe import VllmRecipe
+
+_GLM_4_7_DEFAULTS = {
+    "gpu": "H100",
+    "n_gpu": 8,
+    "extra_vllm_args": ["--trust-remote-code"],
+}
+
+_VLLM_DEFAULTS = VllmRecipe()
+
+
+@dataclass
+class GLM_4_7_VllmRecipe(VllmRecipe):
+    """GLM-4.7 (355B) on 8×H100 — tensor-parallel MoE serving."""
+
+    def __post_init__(self) -> None:
+        for key, val in _GLM_4_7_DEFAULTS.items():
+            if getattr(self, key) == getattr(_VLLM_DEFAULTS, key):
+                object.__setattr__(self, key, val)

modal_training_gym/deploy_recipes/vllm_recipe/glm_4_7_flash.py

@@ -0,0 +1,21 @@
+from dataclasses import dataclass
+
+from modal_training_gym.deploy_recipes.vllm_recipe.recipe import VllmRecipe
+
+_GLM_4_7_FLASH_DEFAULTS = {
+    "gpu": "H100",
+    "n_gpu": 2,
+    "extra_vllm_args": ["--trust-remote-code"],
+}
+
+_VLLM_DEFAULTS = VllmRecipe()
+
+
+@dataclass
+class GLM_4_7_Flash_VllmRecipe(VllmRecipe):
+    """GLM-4.7-Flash on 2×H100 — tensor-parallel MoE serving."""
+
+    def __post_init__(self) -> None:
+        for key, val in _GLM_4_7_FLASH_DEFAULTS.items():
+            if getattr(self, key) == getattr(_VLLM_DEFAULTS, key):
+                object.__setattr__(self, key, val)

modal_training_gym/train_recipes/slime_recipe/__init__.py

@@ -3,13 +3,19 @@
     SlimeRecipeBlock,
 )
 from modal_training_gym.train_recipes.slime_recipe.recipe import SlimeRecipe
+from modal_training_gym.train_recipes.slime_recipe.glm_4_7 import GLM_4_7_Recipe
+from modal_training_gym.train_recipes.slime_recipe.glm_4_7_flash import (
+    GLM_4_7_Flash_Recipe,
+)
 from modal_training_gym.train_recipes.slime_recipe.qwen3_1_7b import Qwen3_1_7b_Recipe
 from modal_training_gym.train_recipes.slime_recipe.qwen3_8b import Qwen3_8b_Recipe
 from modal_training_gym.train_recipes.slime_recipe.qwen3_14b import Qwen3_14b_Recipe
 from modal_training_gym.train_recipes.slime_recipe.qwen3_32b import Qwen3_32b_Recipe
 from modal_training_gym.train_recipes.slime_recipe.qwen3_4b import Qwen3_4b_Recipe
 
 __all__ = [
+    "GLM_4_7_Recipe",
+    "GLM_4_7_Flash_Recipe",
     "MultiTurn",
     "SlimeRecipe",
     "SlimeRecipeBlock",

modal_training_gym/train_recipes/slime_recipe/glm_4_7.py

@@ -0,0 +1,51 @@
+from pydantic import ConfigDict
+from pydantic.dataclasses import dataclass
+
+from modal_training_gym.train_recipes.slime_recipe.recipe import SlimeRecipe
+
+
+@dataclass(config=ConfigDict(extra="forbid", arbitrary_types_allowed=True))
+class GLM_4_7_Recipe(SlimeRecipe):
+    """GLM-4.7 (355B-A32B MoE) on 8×8×H100, colocated GRPO.
+
+    TP=8, PP=4, CP=2, EP=16 across 8 nodes (64 GPUs).
+    Uses CPU optimizer offloading for the large parameter count.
+    """
+
+    gpu_type: str = "H100"
+    colocate: bool = True
+    actor_num_nodes: int = 8
+    actor_num_gpus_per_node: int = 8
+    tensor_model_parallel_size: int = 8
+    sequence_parallel: bool = True
+    rollout_num_gpus_per_engine: int = 32
+
+    # MoE parallelism
+    expert_model_parallel_size: int = 16
+    expert_tensor_parallel_size: int = 1
+    pipeline_model_parallel_size: int = 4
+    context_parallel_size: int = 2
+    attention_backend: str | None = "flash"
+
+    # Rollout
+    num_rollout: int = 1
+    rollout_batch_size: int = 64
+    rollout_max_response_len: int = 4096
+    rollout_temperature: float = 1.0
+    sglang_mem_fraction_static: float = 0.70
+
+    save_interval: int = 10
+
+    # Training
+    n_samples_per_prompt: int = 8
+    global_batch_size: int = 512
+    lr: float = 1e-6
+    max_tokens_per_gpu: int = 16384
+
+    # Optimizer offloading (required for 355B model)
+    optimizer_cpu_offload: bool = True
+    overlap_cpu_optimizer_d2h_h2d: bool = True
+    use_precision_aware_optimizer: bool = True
+
+    eval_interval: int | None = 10
+    eval_max_response_len: int = 4096