Compact 2B-class model
The model has 2,516,756,480 parameters in a dense Transformer, providing a compact alternative for applications that do not require a much larger model.
MiniCPM5-2B
MiniCPM5-2B is a compact 2B-class causal language model for local assistants, coding agents, tool-use workflows, long-context text processing and reasoning tasks. It is designed for on-device and resource-constrained deployment and supports a native 131,072-token context length.
Overview
MiniCPM5-2B is a 2B-class dense causal language model from the MiniCPM5 series. It uses the standard LlamaForCausalLM architecture and is designed for local assistants, coding agents, tool-use workflows and reasoning scenarios where a compact model is useful.
The model combines a small deployment target with a native 131,072-token context length. Its published comparison reports an average score of 53.9 across the stated evaluation set, with tests spanning coding, mathematics, instruction following, knowledge, long-context tasks, tool use and agent workflows. These results describe the authors' comparison set rather than a guarantee for every application.
Key features
Long-context support
A 131,072-token native context length supports tasks involving long documents, extended conversations and other large textual inputs.
Local and edge deployment focus
The model is presented for local assistants, on-device deployment and resource-constrained scenarios, making local execution a central target rather than only a hosted workflow.
Broad task coverage
The published evaluation table covers code reasoning, mathematics, instruction following, general knowledge, long-context understanding, tool use and agent tasks.
Multi-stage post-training and open data
The final checkpoint is described as post-trained with supervised fine-tuning, reinforcement learning and OPD, while the project also publishes related training datasets in the UltraData family.
Multiple runtime formats
The model directory provides BF16, GGUF, MLX/4-bit, GPTQ/4-bit, DSpark and LiteRT-LM variants for different deployment environments.
Use cases
Local assistant
Use the model as a locally run conversational assistant when an application needs language interaction without selecting a larger model by default. The source specifically positions MiniCPM5-2B for on-device and local deployment.
Coding support and coding agents
Apply it to coding-oriented workflows such as code reasoning and coding-agent tasks. The published results include LiveCodeBench, LCB-Pro, OJBench, SciCode and software-engineering agent evaluations, but application-specific validation remains necessary.
Tool-use workflows
Build workflows that combine model responses with external tools or structured actions. Tool-use benchmarks in the model card include τ³-Bench, τ²-Bench and BFCL v4, providing evidence for this intended workflow category.
Long-context text processing
Process long documents, extended conversations or other large text inputs within a single model context. The 131,072-token context length is the relevant published capability for this scenario.
Compact reasoning and agent prototypes
Prototype compact reasoning or agent systems that need coverage across mathematics, general knowledge, search and general-agent tasks. The model card reports evaluations across these areas, while actual suitability depends on the task and runtime.
Pros and Cons
Pros
- Compact 2B-class dense architecture aimed at local and resource-constrained deployment.
- Native 131,072-token context length for long-text workflows.
- Published evaluations cover coding, mathematics, long-context understanding, tool use and several agent categories.
- Available in several formats, including BF16, GGUF, MLX/4-bit, GPTQ/4-bit, DSpark and LiteRT-LM.
- Apache-2.0 license is identified in the model repository metadata.
Cons
- The source material does not provide hardware requirements, measured latency, memory usage or a complete setup workflow, so deployment cost must be validated for the selected runtime and quantization.
- Benchmark results are reported for a specific comparison set and evaluation methodology; they should not be treated as universal performance guarantees.
- The model's broad task coverage does not remove the need to validate outputs for coding, reasoning and tool-use applications.
FAQ
What is MiniCPM5-2B?
MiniCPM5-2B is a causal language model based on the standard LlamaForCausalLM architecture. It is a dense Transformer with 2,516,756,480 parameters, 42 layers, 16 query-attention heads and 2 key/value heads.
What is MiniCPM5-2B designed for?
The model is intended for local assistants, coding agents, tool-use workflows, reasoning tasks, on-device deployment and other resource-constrained scenarios where a compact model is preferred.
How much context does MiniCPM5-2B support?
The model card lists a native context length of 131,072 tokens, supporting long-context language tasks and workflows that need to process comparatively large inputs.
Which model formats are available?
The listed model variants include a BF16 final release, GGUF for llama.cpp, Ollama and LM Studio, MLX/4-bit for Apple Silicon, GPTQ/4-bit, DSpark draft models for inference acceleration, and a LiteRT-LM version. The appropriate choice depends on the target runtime.
What license does the model use?
The model card identifies the license as Apache-2.0. Users should still review the repository's license and accompanying terms before deploying it in a particular project.
Quick Facts
- Category
- Causal language model
- Architecture
- Standard LlamaForCausalLM; dense Transformer
- Parameters
- 2,516,756,480 total; 1,981,982,720 non-embedding
- Context length
- 131,072 tokens
- Attention
- 42 layers; GQA with 16 Q heads and 2 KV heads
- License
- Apache-2.0
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