What are the hardware requirements for CT LLM Base?

CT LLM Base requires approximately 4.3 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 CT LLM Base and how large is it?

CT LLM Base is developed by m-a-p, a model with 2.1b 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 CT LLM Base?

CT LLM Base is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

CT LLM Base by m-a-p — VRAM 4.3GB, 4K context

Name: CT LLM Base
Author: m-a-p

CT LLM Base is an open-source language model by m-a-p. Features: 2.1b LLM, VRAM: 4.3GB, Context: 4K, LLM Explorer Score: 0.12.

Arxiv:2404.04167 Endpoints compatible Llama Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/m-a-p/CT-LLM-Base

CT LLM Base Parameters and Internals

Model Type

causal language model

Use Cases

Considerations:

The model is intended for academic research only.

Additional Notes

CT-LLM-Base is specifically for Chinese language support, focusing on overcoming potential biases in language adaptability.

LLM Name	CT LLM Base
Repository 🤗	https://huggingface.co/m-a-p/CT-LLM-Base
Model Size	2.1b
Required VRAM	4.3 GB
Updated	2026-07-30
Maintainer	m-a-p
Model Type	llama
Model Files	4.3 GB
Model Architecture	LlamaForCausalLM
Context Length	4096
Model Max Length	4096
Transformers Version	4.38.2
Tokenizer Class	BaichuanTokenizer
End of Sentence Token	</s>
Unk Token	<unk>
Vocabulary Size	125824
Torch Data Type	float16

Rank the CT LLM Base 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

Best Alternatives to CT LLM Base

Best Alternatives	Context / RAM	Downloads	Likes
Sft	32K / 4.2 GB	5	0
Kanana 1.5 2.1B Instruct 2505	32K / 4.6 GB	5371	37
Fc Reasoning 2.1B	32K / 4.2 GB	8	0
Kanana 1.5 2.1B Base	32K / 4.2 GB	1534	13
Kanana Safeguard Prompt 2.1B	8K / 4.2 GB	1895	18
Kanana Nano 2.1B Instruct	8K / 4.2 GB	1025	75
Kanana Nano 2.1B Base	8K / 4.2 GB	391	40
DAMI Base Merge 0408	8K / 4.6 GB	7	0
EcomGen 0.0.2v	8K / 8.4 GB	8	0
CT LLM SFT DPO	4K / 0 GB	32	5

Note: green Score (e.g. "73.2") means that the model is better than m-a-p/CT-LLM-Base.