TinyLlama Description
The TinyLlama Project aims to pretrain an 1.1B Llama on 3 trillion tokens. We can achieve this in "just" 90 day using 16 A100-40G graphics cards with some optimization. We used the exact same architecture and tokenizers as Llama 2 TinyLlama is compatible with many open-source Llama projects. TinyLlama has only 1.1B of parameters. This compactness allows TinyLlama to be used for a variety of applications that require a small computation and memory footprint.
TinyLlama Alternatives
Stable LM
StableLM: Stability AI language models
StableLM builds upon our experience with open-sourcing previous language models in collaboration with EleutherAI. This nonprofit research hub. These models include GPTJ, GPTNeoX and the Pythia Suite, which were all trained on The Pile dataset. Cerebras GPT and Dolly-2 are two recent open-source models that continue to build upon these efforts.
StableLM was trained on a new dataset that is three times bigger than The Pile and contains 1.5 trillion tokens. We will provide more details about the dataset at a later date. StableLM's richness allows it to perform well in conversational and coding challenges, despite the small size of its dataset (3-7 billion parameters, compared to GPT-3's 175 billion).
The development of Stable LM 3B broadens the range of applications that are viable on the edge or on home PCs. This means that individuals and companies can now develop cutting-edge technologies with strong conversational capabilities – like creative writing assistance – while keeping costs low and performance high.
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PygmalionAI
PygmalionAI, a community of open-source projects based upon EleutherAI’s GPT-J 6B models and Meta’s LLaMA model, was founded in 2009. Pygmalion AI is designed for roleplaying and chatting. The 7B variant of the Pygmalion AI is currently actively supported. It is based on Meta AI’s LLaMA AI model. Pygmalion's chat capabilities are superior to larger language models that require much more resources. Our curated datasets of high-quality data on roleplaying ensure that your bot is the best RP partner. The model weights as well as the code used to train the model are both open-source. You can modify/re-distribute them for any purpose you like. Pygmalion and other language models run on GPUs because they require fast memory and massive processing to produce coherent text at a reasonable speed.
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Falcon-40B
Falcon-40B is a 40B parameter causal decoder model, built by TII. It was trained on 1,000B tokens from RefinedWeb enhanced by curated corpora. It is available under the Apache 2.0 licence.
Why use Falcon-40B
Falcon-40B is the best open source model available. Falcon-40B outperforms LLaMA, StableLM, RedPajama, MPT, etc. OpenLLM Leaderboard.
It has an architecture optimized for inference with FlashAttention, multiquery and multiquery.
It is available under an Apache 2.0 license that allows commercial use without any restrictions or royalties.
This is a raw model that should be finetuned to fit most uses. If you're looking for a model that can take generic instructions in chat format, we suggest Falcon-40B Instruct.
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Baichuan-13B
Baichuan-13B, a large-scale language model with 13 billion parameters that is open source and available commercially by Baichuan Intelligent, was developed following Baichuan -7B. It has the best results for a language model of the same size in authoritative Chinese and English benchmarks. This release includes two versions of pretraining (Baichuan-13B Base) and alignment (Baichuan-13B Chat).
Baichuan-13B has more data and a larger size. It expands the number parameters to 13 billion based on Baichuan -7B, and trains 1.4 trillion coins on high-quality corpus. This is 40% more than LLaMA-13B. It is open source and currently the model with the most training data in 13B size. Support Chinese and English bi-lingual, use ALiBi code, context window is 4096.
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Pricing
Pricing Starts At:
Free
Pricing Information:
Open source
Free Version:
Yes
Integrations
No Integrations at this time
Company Details
Company:
TinyLlama
Website:
github.com/jzhang38/TinyLlama
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Product Details
Platforms
Windows
Mac
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Type of Training
Documentation
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