MODEL PROFILE

LaMini-GPT-124M

MBZUAI · LaMini-GPT-124M

This model is one of our LaMini-LM model series in paper "LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions". This model is a fine-tuned version of gpt2 on LaMini-instruction dataset that contains 2.58M samples for instruction fine-tuning. For more information about our dataset, please refer to our project repository. You can view other models of LaMini-LM series as follows. Models with ✩ are those with the best overall performance given their size/architecture, hence we recommend using them. More details can be seen in our paper. We recommend using the model to respond to human instructions written in natural language. Since this decoder-only model is fine-tuned with wrapper text, we suggest using the same wrapper text to achieve the best performance. See the example on the right or the code below. We initialize with gpt2 and fine-tune it on our LaMini-instruction dataset. Its total number of parameters is 124M.

Text generationlicense:cc-by-nc-4.0
Context windowNot disclosed
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/10/30 14:59:55
OVERVIEW

Model overview

This model is one of our LaMini-LM model series in paper "LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions". This model is a fine-tuned version of gpt2 on LaMini-instruction dataset that contains 2.58M samples for instruction fine-tuning. For more information about our dataset, please refer to our project repository. You can view other models of LaMini-LM series as follows. Models with ✩ are those with the best overall performance given their size/architecture, hence we recommend using them. More details can be seen in our paper. We recommend using the model to respond to human instructions written in natural language. Since this decoder-only model is fine-tuned with wrapper text, we suggest using the same wrapper text to achieve the best performance. See the example on the right or the code below. We initialize with gpt2 and fine-tune it on our LaMini-instruction dataset. Its total number of parameters is 124M.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

Source / channelInputOutputBilling unitCollected at
No verified prices yet. Check the source channel.

Estimate monthly base token costs

—

Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.

INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

View full indexed rankings →

Frequently asked questions

Do open weights permit free commercial use?

Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.

Are these real-time billing prices?

These are timestamped source snapshots, not live billing prices. Check the channel for current versions, regions, discounts and billing methods.

Sources & traceability

许可证:cc-by-nc-4.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。