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RichardErkhov/
TencentARC_-_LLaMA-Pro-8B-Instruct-gguf

Name: TencentARC_-_LLaMA-Pro-8B-Instruct-gguf
Availability: InStock

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LLaMA-Pro-8B-Instruct - GGUF

Model creator: https://huggingface.co/TencentARC/
Original model: https://huggingface.co/TencentARC/LLaMA-Pro-8B-Instruct/

Name	Quant method	Size
LLaMA-Pro-8B-Instruct.Q2_K.gguf	Q2_K	2.91GB
LLaMA-Pro-8B-Instruct.IQ3_XS.gguf	IQ3_XS	3.22GB
LLaMA-Pro-8B-Instruct.IQ3_S.gguf	IQ3_S	3.39GB
LLaMA-Pro-8B-Instruct.Q3_K_S.gguf	Q3_K_S	3.39GB
LLaMA-Pro-8B-Instruct.IQ3_M.gguf	IQ3_M	3.59GB
LLaMA-Pro-8B-Instruct.Q3_K.gguf	Q3_K	3.8GB
LLaMA-Pro-8B-Instruct.Q3_K_M.gguf	Q3_K_M	3.8GB
LLaMA-Pro-8B-Instruct.Q3_K_L.gguf	Q3_K_L	4.15GB
LLaMA-Pro-8B-Instruct.IQ4_XS.gguf	IQ4_XS	4.2GB
LLaMA-Pro-8B-Instruct.Q4_0.gguf	Q4_0	4.41GB
LLaMA-Pro-8B-Instruct.IQ4_NL.gguf	IQ4_NL	4.44GB
LLaMA-Pro-8B-Instruct.Q4_K_S.gguf	Q4_K_S	4.45GB
LLaMA-Pro-8B-Instruct.Q4_K.gguf	Q4_K	4.71GB
LLaMA-Pro-8B-Instruct.Q4_K_M.gguf	Q4_K_M	4.71GB
LLaMA-Pro-8B-Instruct.Q4_1.gguf	Q4_1	4.89GB
LLaMA-Pro-8B-Instruct.Q5_0.gguf	Q5_0	5.37GB
LLaMA-Pro-8B-Instruct.Q5_K_S.gguf	Q5_K_S	5.37GB
LLaMA-Pro-8B-Instruct.Q5_K.gguf	Q5_K	5.52GB
LLaMA-Pro-8B-Instruct.Q5_K_M.gguf	Q5_K_M	5.52GB
LLaMA-Pro-8B-Instruct.Q5_1.gguf	Q5_1	5.85GB
LLaMA-Pro-8B-Instruct.Q6_K.gguf	Q6_K	6.39GB
LLaMA-Pro-8B-Instruct.Q8_0.gguf	Q8_0	8.27GB

Original model description:

license: llama2

LLaMA-PRO-Instruct Model Card

Model Description

LLaMA-PRO-Instruct is a transformative expansion of the LLaMA2-7B model, now boasting 8.3 billion parameters. It uniquely specializes in programming, coding, and mathematical reasoning, maintaining versatility in general language tasks.

Development and Training

This model, developed by Tencent ARC team, extends LLaMA2-7B using innovative block expansion techniques. It's meticulously trained on a diverse blend of coding and mathematical data, encompassing over 80 billion tokens.

Intended Use

LLaMA-PRO-Instruct is ideal for complex NLP challenges, excelling in programming, mathematical reasoning, and general language processing, suitable for both specialized and broad applications.

Performance

It surpasses its predecessors in the LLaMA series, especially in code domains, demonstrating exceptional competence as a comprehensive language model.

Limitations

Despite advancements, it may encounter difficulties in highly niche or nuanced tasks.

Ethical Considerations

Users are advised to consider inherent biases and responsibly manage its application across various fields.

RichardErkhov/TencentARC_-_LLaMA-Pro-8B-Instruct-gguf