What are the hardware requirements for MiniCPM 2B Sft Bf16 Llama Format?

MiniCPM 2B Sft Bf16 Llama Format requires approximately 6 GB of VRAM and supports a context window of 4K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed MiniCPM 2B Sft Bf16 Llama Format and how large is it?

MiniCPM 2B Sft Bf16 Llama Format is developed by openbmb, a model with 2b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate MiniCPM 2B Sft Bf16 Llama Format?

MiniCPM 2B Sft Bf16 Llama Format is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

MiniCPM 2B Sft Bf16 Llama Format by openbmb — VRAM 6GB, 4K context

Name: MiniCPM 2B Sft Bf16 Llama Format
Author: openbmb

MiniCPM 2B Sft Bf16 Llama Format is an open-source language model by openbmb. Features: 2b LLM, VRAM: 6GB, Context: 4K, LLM Explorer Score: 0.11.

Conversational En Endpoints compatible Minicpm Modelbest Pytorch Region:us Thunlp Zh

Model Card on HF 🤗: https://huggingface.co/openbmb/MiniCPM-2B-sft-bf16-llama-format

MiniCPM 2B Sft Bf16 Llama Format Parameters and Internals

Model Type

End-Side LLM

Use Cases

Areas:

academic research

Applications:

Mobile Phone Applications

Limitations:

Hallucinatory issues due to model size, Identity information outputs from ShareGPT corpus

Considerations:

Combining RAG method to enhance memory.

Additional Notes

All model parameters for research and limited commercial use.

Supported Languages

languages_supported (Chinese, Mathematics, Coding), proficiency_level (High)

Training Details

Data Sources:

ShareGPT open-source corpus

Methodology:

SFT and DPO

Safety Evaluation

Risk Categories:

hallucinatory issues, model identity bias

Input Output

Input Format:

Prompt-based input

Accepted Modalities:

text, multimodal

Output Format:

Language and Multimodal generation

Performance Tips:

Specify model datatype in 'from_pretrained' to avoid calculation errors.

LLM Name	MiniCPM 2B Sft Bf16 Llama Format
Repository 🤗	https://huggingface.co/openbmb/MiniCPM-2B-sft-bf16-llama-format
Model Size	2b
Required VRAM	6 GB
Updated	2026-07-24
Maintainer	openbmb
Model Files	6.0 GB
Supported Languages	en zh
Model Architecture	LlamaForCausalLM
Context Length	4096
Model Max Length	4096
Transformers Version	4.36.0
Tokenizer Class	LlamaTokenizer
Vocabulary Size	122753
Torch Data Type	bfloat16

Rank the MiniCPM 2B Sft Bf16 Llama Format capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

Quantized Models of the MiniCPM 2B Sft Bf16 Llama Format

Model	Likes	Downloads	VRAM
...guf MINICPM Dqg V03042024 Rev2	0	59	2 GB

Best Alternatives to MiniCPM 2B Sft Bf16 Llama Format

Best Alternatives	Context / RAM	Downloads	Likes
Llama 2B Hf 32768 Fpf	32K / 3.8 GB	8	1
...icpm 2B Sft Bf16 Llamafied 16K	16K / 6 GB	560	1
...de Toolcall Slm 2B Safetensors	8K / 3.4 GB	0	1
Aitana 2B SI Instruct	8K / 4.5 GB	52	0
Aitana 2B S Base IP 1.0	8K / 4.5 GB	186	0
Aitana 2B SI Instruct Aligned	8K / 4.5 GB	19	0
Aitana 2B S IP Instruct	8K / 4.5 GB	13	0
Aitana 2B S Instruct Aligned	8K / 6.9 GB	10	0
Aitana 2B S IP Base	8K / 4.5 GB	13	0
Salamandra 2B Fft Trenes	8K / 4.5 GB	32	0

Note: green Score (e.g. "73.2") means that the model is better than openbmb/MiniCPM-2B-sft-bf16-llama-format.