--- title: LocoOperator-4B-MLX canonical_url: "https://www.modelscope.cn/models/chongd/LocoOperator-4B-MLX" md_url: "https://www.modelscope.cn/models/chongd/LocoOperator-4B-MLX.md" repository: chongd/LocoOperator-4B-MLX last_updated: 2026-03-26 license: mit pipeline_tag: text-generation tasks: - text-generation model_type: - qwen3 architectures: - Qwen3ForCausalLM base_model: - LocoreMind/LocoOperator-4B base_model_relation: finetune parameters: 4.0B tensor_type: - BF16 library_name: - mlx - safetensors language: - en inference_backends: - "deploy_task text/emb" - "lmdeploy_turbomind 0.9.1" - "sglang 0.5.2" - "vllm 0.9.2" downloads: 24 stars: 0 tags: - code - agent - tool-calling - distillation - qwen3 - gguf - llama-cpp - mlx --- # LocoOperator-4B-MLX > LocoOperator-4B-MLX - chongd 在 ModelScope 开源的模型。MLX format of model LocoreMind/LocoOperator-4B. chongd/LocoOperator-4B-MLX 是 ModelScope 魔搭社区上的 4.0B 参数text-generation模型，采用 mit 许可，基于 LocoreMind/LocoOperator-4B 构建，可用 deploy_task text/emb、lmdeploy_turbomind 0.9.1、sglang 0.5.2 部署。 - **Repository**: chongd/LocoOperator-4B-MLX - **License**: mit - **Tasks**: text-generation - **Parameters**: 4.0B - **Base model**: LocoreMind/LocoOperator-4B - **Inference backends**: deploy_task text/emb, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2 - **Tags**: code, agent, tool-calling, distillation, qwen3, gguf, llama-cpp, mlx - **Downloads**: 24 - **Stars**: 0 - **Last updated**: 2026-03-26 Source: https://www.modelscope.cn/models/chongd/LocoOperator-4B-MLX --- MLX format of model [LocoreMind/LocoOperator-4B](https://huggingface.co/LocoreMind/LocoOperator-4B). --- ```plaintext Apple MacBook M3 Pro 内存：36GB 核总数：14 ``` Intelligence Benchmark Comparison | Benchmark | Mode | Sampled | [Qwen3-8B-MLX-8bit](https://modelscope.cn/models/Qwen/Qwen3-8B-MLX-8bit) | [Nanbeige4.1-3B](https://modelscope.cn/models/chongd/Nanbeige4.1-3B) | [Qwen3-4B-MLX-bf16](https://modelscope.cn/models/Qwen/Qwen3-4B-MLX-bf16) | [AgentCPM-Explore](https://modelscope.cn/models/chongd/AgentCPM-Explore-MLX) | [**LocoOperator-4B**](https://modelscope.cn/models/chongd/LocoOperator-4B-MLX) | [Qwen3.5-4B-Claude-4.6-Opus-Reasoning-Distilled](https://modelscope.cn/models/chongd/Qwen3.5-4B-Claude-4.6-Opus-Reasoning-Distilled-MLX) | |-----------|------|---------|-------------------|----------------|-------------------|------------------|-----------------|------------------------------------------------| | MMLU | Sample | 1000/14042 | 69.6% | 24.4% | 66.4% | 23.4% | 69.5% | 23.9% | | CMMLU | Sample | 300/11582 | 77.0% | 26.3% | 65.7% | 38.3% | 67.0% | 22.0% | | TRUTHFULQA | Full | 817 | 64.8% | 22.4% | 55.9% | 26.9% | 59.5% | 24.2% | | HUMANEVAL | Full | 164 | 76.8% | 0.0% | 76.2% | 67.7% | 83.5% | 53.7% | | HELLASWAG | Sample | 200/10042 | 77.5% | 21.5% | 55.0% | 25.5% | 82.5% | 24.5% | | WINOGRANDE | Sample | 300/1267 | 58.7% | 50.7% | 56.3% | 50.0% | 64.3% | 49.0% | --- Detail --- ```plaintext Model: Qwen3-8B-MLX-8bit Benchmark Accuracy Correct Total Time(s) ------------------------------------------------------ MMLU 69.6% 696 1000 2001.9 CMMLU 77.0% 231 300 401.7 TRUTHFULQA 64.8% 529 817 735.8 HUMANEVAL 76.8% 126 164 447.3 WINOGRANDE 58.7% 176 300 163.3 HELLASWAG 77.5% 155 200 266.1 Model: Nanbeige4.1-3B Benchmark Accuracy Correct Total Time(s) ------------------------------------------------------ MMLU 24.4% 244 1000 11826.9 CMMLU 26.3% 79 300 1027.5 HELLASWAG 21.5% 43 200 713 TRUTHFULQA 22.4% 183 817 2768.1 WINOGRANDE 50.7% 152 300 1026.4 HUMANEVAL 0.0% 0 164 2153.9 Model: Qwen3-4B-MLX-bf16 Benchmark Accuracy Correct Total Time(s) ------------------------------------------------------ MMLU 66.4% 664 1000 1292.1 CMMLU 65.7% 197 300 616.7 HELLASWAG 55.0% 110 200 373.5 TRUTHFULQA 55.9% 457 817 785.9 WINOGRANDE 56.3% 169 300 149.5 HUMANEVAL 76.2% 125 164 745.1 Model: AgentCPM-Explore Benchmark Accuracy Correct Total Time(s) ------------------------------------------------------ MMLU 23.4% 234 1000 4683.5 CMMLU 38.3% 115 300 1216.4 HELLASWAG 25.5% 51 200 895.8 TRUTHFULQA 26.9% 220 817 3468.7 WINOGRANDE 50.0% 150 300 1635 HUMANEVAL 67.7% 111 164 4067.7 Model: LocoOperator-4B Benchmark Accuracy Correct Total Time(s) ------------------------------------------------------ MMLU 69.5% 695 1000 1175.1 CMMLU 67.0% 201 300 338.9 HELLASWAG 82.5% 165 200 144.2 TRUTHFULQA 59.5% 486 817 934.4 WINOGRANDE 64.3% 193 300 149.6 HUMANEVAL 83.5% 137 164 829.7 Model: Qwen3.5-4B-Claude-4.6-Opus-Reasoning-Distilled Benchmark Accuracy Correct Total Time(s) ------------------------------------------------------ MMLU 23.9% 239 1000 5595.6 CMMLU 22.0% 66 300 1374.5 HELLASWAG 24.5% 49 200 1832.6 TRUTHFULQA 24.2% 198 817 3914.1 WINOGRANDE 49.0% 147 300 1370.1 HUMANEVAL 53.7% 88 164 2680.2 ```