Supported Models
docs.perplexity.ai
Supported Models
Llama 2 - Resource Overview - Meta AI
ai.meta.com
Advances in data processing techniques. You can increase context length in two ways. First, you can train the model with longer context lengths. That’s difficult because it’s much more computationally expensive, and it’s hard to find datasets with long context lengths (most documents in CommonCrawl have fewer than 2,000 tokens).
The second, more com... See more
The second, more com... See more
Shortwave — rajhesh.panchanadhan@gmail.com [Gmail alternative]
Model Description
NSQL is a family of autoregressive open-source large foundation models (FMs) designed specifically for SQL generation tasks.
In this repository we are introducing a new member of NSQL, NSQL-Llama-2-7B. It's based on Meta's original Llama-2 7B model and further pre-trained on a dataset of general SQL queries and then fine-tuned on a ... See more
NSQL is a family of autoregressive open-source large foundation models (FMs) designed specifically for SQL generation tasks.
In this repository we are introducing a new member of NSQL, NSQL-Llama-2-7B. It's based on Meta's original Llama-2 7B model and further pre-trained on a dataset of general SQL queries and then fine-tuned on a ... See more
NumbersStation/nsql-llama-2-7B · Hugging Face
Stable Beluga 2
Use Stable Chat (Research Preview) to test Stability AI's best language models for free
Model Description
Stable Beluga 2 is a Llama2 70B model finetuned on an Orca style Dataset
Use Stable Chat (Research Preview) to test Stability AI's best language models for free
Model Description
Stable Beluga 2 is a Llama2 70B model finetuned on an Orca style Dataset
stabilityai/StableBeluga2 · Hugging Face
eneral-purpose models
- 1.1B: TinyDolphin 2.8 1.1B. Takes about ~700MB RAM and tested on my Pi 4 with 2 gigs of RAM. Hallucinates a lot, but works for basic conversation.
- 2.7B: Dolphin 2.6 Phi-2. Takes over ~2GB RAM and tested on my 3GB 32-bit phone via llama.cpp on Termux.
- 7B: Nous Hermes Mistral 7B DPO. Takes about ~4-5GB RAM depending on contex
r/LocalLLaMA - Reddit
TL;DR
LLMLingua utilizes a compact, well-trained language model (e.g., GPT2-small, LLaMA-7B) to identify and remove non-essential tokens in prompts. This approach enables efficient inference with large language models (LLMs), achieving up to 20x compression with minimal performance loss.
... See more
LLMLingua utilizes a compact, well-trained language model (e.g., GPT2-small, LLaMA-7B) to identify and remove non-essential tokens in prompts. This approach enables efficient inference with large language models (LLMs), achieving up to 20x compression with minimal performance loss.
... See more
microsoft • GitHub - microsoft/LLMLingua: To speed up LLMs' inference and enhance LLM's perceive of key information, compress the prompt and KV-Cache, which achieves up to 20x compression with minimal performance loss.
- LLM Pro/Serious Use Comparison/Test: From 7B to 70B vs. ChatGPT! Winner: Synthia-70B-v1.2b
- LLM Chat/RP Comparison/Test: Dolphin-Mistral, Mistral-OpenOrca, Synthia 7B Winner: Mistral-7B-OpenOrca
- LLM Chat/RP Comparison/Test: Mistral 7B Base + Instruct
- LLM Chat/RP Comparison/Test (Euryale, FashionGPT, MXLewd, Synthia, Xwin) Winner: Xwin-LM-70B-V0.1
- New Mo