diff --git a/README.md b/README.md
index 7be5fc7f47d5db027d120b8024982df93db95b74..4390000ebbda4f736ce11a64a0ab861d244d87f2 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,137 @@
----
-license: mit
----
+---
+language:
+- en
+library_name: transformers
+tags:
+- glm
+- MOE
+- pruning
+- compression
+license: mit
+name: cerebras/GLM-4.7-REAP-268B-A32B
+description: >
+ This model was obtained by uniformly pruning 25% of experts in GLM-4.7 using the REAP method.
+readme: >
+ https://huggingface.co/cerebras/GLM-4.7-REAP-268B-A32B/main/README.md
+license_link: https://huggingface.co/zai-org/GLM-4.7/blob/main/LICENSE
+pipeline_tag: text-generation
+base_model:
+- zai-org/GLM-4.7
+---
+
+
+ ๐ณ REAP๐ณ the Experts: Why Pruning Prevails for One-Shot MoE Compression
+
+
+
+# GLM-4.7-REAP-268B-A32B
+
+## โจ Highlights
+
+Introducing **GLM-4.7-REAP-268B-A32B**, a **memory-efficient compressed variant** of GLM-4.7 that maintains near-identical performance while being **25% lighter**.
+
+This model was created using **REAP (Router-weighted Expert Activation Pruning)**, a novel expert pruning method that selectively removes redundant experts while preserving the router's independent control over remaining experts. Key features include:
+
+- **Near-Lossless Performance**: Maintains almost identical accuracy on code generation, agentic coding, and function calling tasks compared to the full 355B model
+- **25% Memory Reduction**: Compressed from 355B to 268B parameters, significantly lowering deployment costs and memory requirements
+- **Preserved Capabilities**: Retains all core functionalities including code generation, agentic workflows, repository-scale understanding, and function calling
+- **Drop-in Compatibility**: Works with vanilla vLLM - no source modifications or custom patches required
+- **Optimized for Real-World Use**: Particularly effective for resource-constrained environments, local deployments, and academic research
+
+**For downstream low-bit quantization, we suggest using the [BF16 variant](https://huggingface.co/cerebras/GLM-4.7-REAP-268B-A32B).**
+
+---
+## ๐ Model Overview
+
+**GLM-4.7-REAP-268B-A32B** has the following specifications:
+
+- **Base Model**: GLM-4.7
+- **Compression Method**: REAP (Router-weighted Expert Activation Pruning)
+- **Compression Ratio**: 25% expert pruning
+- **Type**: Sparse Mixture-of-Experts (SMoE) Causal Language Model
+- **Number of Parameters**: 268B total, 32B activated per token
+- **Number of Layers**: 92
+- **Number of Attention Heads (GQA)**: 96 for Q and 8 for KV
+- **Number of Experts**: 120 (uniformly pruned from 160)
+- **Number of Activated Experts**: 8 per token
+- **Context Length**: 202,752 tokens
+- **License**: MIT
+
+---
+
+## ๐ Evaluations
+
+TBD for BF16 model. [Evalulation results available for the FP8 variant](https://huggingface.co/cerebras/GLM-4.7-REAP-268B-A32B-FP8#%F0%9F%93%8A-evaluations).
+
+For more details on the evaluation setup, refer to the [REAP arXiv preprint](https://arxiv.org/abs/2510.13999).
+
+---
+
+## ๐ Deployment
+
+You can deploy the model directly using the **latest vLLM** (v0.11.0), no source modifications or custom patches required.
+
+```bash
+vllm serve cerebras/GLM-4.7-REAP-268B-A32B \
+ --tensor-parallel-size 8 \
+ --tool-call-parser glm45 \
+ --enable-auto-tool-choice \
+ --enable-expert-parallel
+```
+
+If you encounter insufficient memory when running this model, you might need to set a lower value for `--max-num-seqs` flag (e.g. set to 64).
+
+
+## ๐งฉ Model Creation
+
+This checkpoint was created by applying the **REAP (Router-weighted Expert Activation Pruning)** method uniformly across all Mixture-of-Experts (MoE) blocks of **GLM-4.7**, with a **25% pruning rate**.
+
+### How REAP Works
+
+REAP selects experts to prune based on a novel **saliency criterion** that considers both:
+- **Router gate values**: How frequently and strongly the router activates each expert
+- **Expert activation norms**: The magnitude of each expert's output contributions
+
+This dual consideration ensures that experts contributing minimally to the layer's output are pruned, while preserving those that play critical roles in the model's computations.
+
+### Key Advantages
+
+- **One-Shot Compression**: No fine-tuning required after pruning - the model is immediately ready for deployment
+- **Preserved Router Control**: Unlike expert merging methods, REAP maintains the router's independent, input-dependent control over remaining experts, avoiding "functional subspace collapse"
+- **Generative Task Superiority**: REAP significantly outperforms expert merging approaches on generative benchmarks (code generation, creative writing, mathematical reasoning) while maintaining competitive performance on discriminative tasks
+
+### Calibration
+
+The model was calibrated using a diverse mixture of domain-specific datasets including:
+- Code generation samples ([evol-codealpaca](https://huggingface.co/datasets/theblackcat102/evol-codealpaca-v1))
+- Function calling examples ([xlam-function-calling](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k))
+- Agentic multi-turn trajectories ([SWE-smith-trajectories](https://huggingface.co/datasets/SWE-bench/SWE-smith-trajectories))
+
+๐ For more details, refer to the following resources:
+
+- [๐งพ arXiv Preprint](https://arxiv.org/abs/2510.13999)
+- [๐งพ REAP Blog](https://www.cerebras.ai/blog/reap)
+- [๐ป REAP Codebase (GitHub)](https://github.com/CerebrasResearch/reap)
+
+---
+
+## โ๏ธ License
+
+This model is derived from
+**[`zai-org/GLM-4.7`](https://huggingface.co/zai-org/GLM-4.7)**
+and distributed under the **MIT license**.
+
+---
+
+## ๐งพ Citation
+
+If you use this checkpoint, please cite the REAP paper:
+
+```bibtex
+@article{lasby-reap,
+ title={REAP the Experts: Why Pruning Prevails for One-Shot MoE compression},
+ author={Lasby, Mike and Lazarevich, Ivan and Sinnadurai, Nish and Lie, Sean and Ioannou, Yani and Thangarasa, Vithursan},
+ journal={arXiv preprint arXiv:2510.13999},
+ year={2025}
+}
+```
\ No newline at end of file
diff --git a/chat_template.jinja b/chat_template.jinja
new file mode 100644
index 0000000000000000000000000000000000000000..2ab98ef068d62829d17c5ade1827b9f013fa2bbf
--- /dev/null
+++ b/chat_template.jinja
@@ -0,0 +1,86 @@
+[gMASK]
+{%- if tools -%}
+<|system|>
+# Tools
+
+You may call one or more functions to assist with the user query.
+
+You are provided with function signatures within XML tags:
+
+{% for tool in tools %}
+{{ tool | tojson(ensure_ascii=False) }}
+{% endfor %}
+
+
+For each function call, output the function name and arguments within the following XML format:
+{function-name}{arg-key-1}{arg-value-1}{arg-key-2}{arg-value-2}...{%- endif -%}
+{%- macro visible_text(content) -%}
+ {%- if content is string -%}
+ {{- content }}
+ {%- elif content is iterable and content is not mapping -%}
+ {%- for item in content -%}
+ {%- if item is mapping and item.type == 'text' -%}
+ {{- item.text }}
+ {%- elif item is string -%}
+ {{- item }}
+ {%- endif -%}
+ {%- endfor -%}
+ {%- else -%}
+ {{- content }}
+ {%- endif -%}
+{%- endmacro -%}
+{%- set ns = namespace(last_user_index=-1) %}
+{%- for m in messages %}
+ {%- if m.role == 'user' %}
+ {% set ns.last_user_index = loop.index0 -%}
+ {%- endif %}
+{%- endfor %}
+{% for m in messages %}
+{%- if m.role == 'user' -%}<|user|>{{ visible_text(m.content) }}
+{%- elif m.role == 'assistant' -%}
+<|assistant|>
+{%- set reasoning_content = '' %}
+{%- set content = visible_text(m.content) %}
+{%- if m.reasoning_content is string %}
+ {%- set reasoning_content = m.reasoning_content %}
+{%- else %}
+ {%- if '' in content %}
+ {%- set reasoning_content = content.split('')[0].rstrip('\n').split('')[-1].lstrip('\n') %}
+ {%- set content = content.split('')[-1].lstrip('\n') %}
+ {%- endif %}
+{%- endif %}
+{%- if ((clear_thinking is defined and not clear_thinking) or loop.index0 > ns.last_user_index) and reasoning_content -%}
+{{ '' + reasoning_content.strip() + ''}}
+{%- else -%}
+{{ '' }}
+{%- endif -%}
+{%- if content.strip() -%}
+{{ content.strip() }}
+{%- endif -%}
+{% if m.tool_calls %}
+{% for tc in m.tool_calls %}
+{%- if tc.function %}
+ {%- set tc = tc.function %}
+{%- endif %}
+{{- '' + tc.name -}}
+{% set _args = tc.arguments %}{% for k, v in _args.items() %}{{ k }}{{ v | tojson(ensure_ascii=False) if v is not string else v }}{% endfor %}{% endfor %}
+{% endif %}
+{%- elif m.role == 'tool' -%}
+{%- if m.content is string -%}
+{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+ {{- '<|observation|>' }}
+{%- endif %}
+{{- '' }}
+{{- m.content }}
+{{- '' }}
+{%- else -%}
+<|observation|>{% for tr in m.content %}
+{{ tr.output if tr.output is defined else tr }}{% endfor -%}
+{% endif -%}
+{%- elif m.role == 'system' -%}
+<|system|>{{ visible_text(m.content) }}
+{%- endif -%}
+{%- endfor -%}
+{%- if add_generation_prompt -%}
+ <|assistant|>{{- '' if (enable_thinking is defined and not enable_thinking) else '' -}}
+{%- endif -%}
\ No newline at end of file
diff --git a/config.json b/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..97624f55f955bc001ef4ff42e932b862c35a92d4
--- /dev/null
+++ b/config.json
@@ -0,0 +1,43 @@
+{
+ "architectures": [
+ "Glm4MoeForCausalLM"
+ ],
+ "attention_bias": true,
+ "attention_dropout": 0.0,
+ "eos_token_id": [
+ 151329,
+ 151336,
+ 151338
+ ],
+ "first_k_dense_replace": 3,
+ "head_dim": 128,
+ "hidden_act": "silu",
+ "hidden_size": 5120,
+ "initializer_range": 0.02,
+ "intermediate_size": 12288,
+ "max_position_embeddings": 202752,
+ "model_type": "glm4_moe",
+ "moe_intermediate_size": 1536,
+ "n_group": 1,
+ "n_routed_experts": 120,
+ "n_shared_experts": 1,
+ "norm_topk_prob": true,
+ "num_attention_heads": 96,
+ "num_experts_per_tok": 8,
+ "num_hidden_layers": 92,
+ "num_key_value_heads": 8,
+ "num_nextn_predict_layers": 0,
+ "pad_token_id": 151329,
+ "partial_rotary_factor": 0.5,
+ "rms_norm_eps": 1e-05,
+ "rope_scaling": null,
+ "rope_theta": 1000000,
+ "routed_scaling_factor": 2.5,
+ "tie_word_embeddings": false,
+ "topk_group": 1,
+ "torch_dtype": "bfloat16",
+ "transformers_version": "4.55.0",
+ "use_cache": true,
+ "use_qk_norm": true,
+ "vocab_size": 151552
+}
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