What are the hardware requirements for Chinese Alpaca 2 1.3B Rlhf?

Chinese Alpaca 2 1.3B Rlhf requires approximately 2.5 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 Chinese Alpaca 2 1.3B Rlhf and how large is it?

Chinese Alpaca 2 1.3B Rlhf is developed by hfl, a model with 7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Chinese Alpaca 2 1.3B Rlhf perform on standard benchmarks?

Chinese Alpaca 2 1.3B Rlhf has the following published scores: MMLU 26.53. Compare against reference models on this page or on the LLM Explorer leaderboards.

Chinese Alpaca 2 1.3B Rlhf by hfl — VRAM 2.5GB, 4K context

Name: Chinese Alpaca 2 1.3B Rlhf
Author: hfl

Chinese Alpaca 2 1.3B Rlhf is an open-source language model by hfl. Features: 7b LLM, VRAM: 2.5GB, Context: 4K, License: apache-2.0, LLM Explorer Score: 0.15, Arc: 23.9, HellaSwag: 30, MMLU: 26.5.

En Endpoints compatible Llama Pytorch Region:us Safetensors Zh

Model Card on HF 🤗: https://huggingface.co/hfl/chinese-alpaca-2-1.3b-rlhf

Chinese Alpaca 2 1.3B Rlhf Parameters and Internals

Additional Notes

This model is tuned with Reinforcement Learning from Human Feedback (RLHF) using DeepSpeed-Chat.

Supported Languages

zh (native), en (native)

LLM Name	Chinese Alpaca 2 1.3B Rlhf
Repository 🤗	https://huggingface.co/hfl/chinese-alpaca-2-1.3b-rlhf
Model Size	7b
Required VRAM	2.5 GB
Updated	2026-08-06
Maintainer	hfl
Model Type	llama
Model Files	2.5 GB 2.5 GB
Supported Languages	zh en
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	4096
Model Max Length	4096
Transformers Version	4.35.1
Tokenizer Class	LlamaTokenizer
Beginning of Sentence Token	<s>
End of Sentence Token	</s>
Unk Token	<unk>
Vocabulary Size	55296
Torch Data Type	float16

Rank the Chinese Alpaca 2 1.3B Rlhf 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

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Note: green Score (e.g. "73.2") means that the model is better than hfl/chinese-alpaca-2-1.3b-rlhf.