LlamaCorn-1.1B-Chat-GGUF

QuantFactory/LlamaCorn-1.1B-Chat-GGUF

This is quantized version of jan-hq/LlamaCorn-1.1B-Chat created using llama.cpp

Original Model Card

<!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto" > <img src="https://github.com/janhq/jan/assets/89722390/35daac7d-b895-487c-a6ac-6663daaad78e" alt="Jan banner" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <p align="center"> <a href="https://jan.ai/">Jan</a > - <a href="https://discord.gg/AsJ8krTT3N">Discord</a> </p> <!-- header end -->

Model description

• Finetuned TinyLlama-1.1B further for handling simple tasks and have acceptable conversational quality
• Utilized high-quality opensource dataset
• Can be run on TensorRT-LLM on consumer devices
• Can fit into laptop dGPUs with as little as >=6gb of VRAM

Prompt template

ChatML

<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Run this model

You can run this model using Jan Desktop on Mac, Windows, or Linux.

Jan is an open source, ChatGPT alternative that is:

• 💻 100% offline on your machine: Your conversations remain confidential, and visible only to you.

• 🗂️ ** An Open File Format**: Conversations and model settings stay on your computer and can be exported or deleted at any time.

• 🌐 OpenAI Compatible: Local server on port 1337 with OpenAI compatible endpoints

• 🌍 Open Source & Free: We build in public; check out our Github

About Jan

Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones.

Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life.

LlamaCorn-1.1B-Chat

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-07
• train_batch_size: 2
• eval_batch_size: 4
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• gradient_accumulation_steps: 16
• total_train_batch_size: 64
• total_eval_batch_size: 8
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 3

Training results

Framework versions

• Transformers 4.36.2
• Pytorch 2.1.2+cu121
• Datasets 2.14.6
• Tokenizers 0.15.0

Open LLM Leaderboard Evaluation Results

Detailed results can be found here