AmberChat

We present AmberChat, an instruction following model finetuned from LLM360/Amber. AmberChat is part of LLM360's Pebble model series.

Evaluation

Model Description

• Model type: Language model with the same architecture as LLaMA-7B
• Language(s) (NLP): English
• License: Apache 2.0
• Resources for more information:
  • Metrics
  • Fully processed Amber pretraining data
  • Finetuning Code

Loading AmberChat

import torch
from transformers import LlamaTokenizer, LlamaForCausalLM

tokenizer = LlamaTokenizer.from_pretrained("LLM360/AmberChat")
model = LlamaForCausalLM.from_pretrained("LLM360/AmberChat")

#template adapated from fastchat
template= "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\n### Human: Got any creative ideas for a 10 year old’s birthday?\n### Assistant: Of course! Here are some creative ideas for a 10-year-old's birthday party:\n1. Treasure Hunt: Organize a treasure hunt in your backyard or nearby park. Create clues and riddles for the kids to solve, leading them to hidden treasures and surprises.\n2. Science Party: Plan a science-themed party where kids can engage in fun and interactive experiments. You can set up different stations with activities like making slime, erupting volcanoes, or creating simple chemical reactions.\n3. Outdoor Movie Night: Set up a backyard movie night with a projector and a large screen or white sheet. Create a cozy seating area with blankets and pillows, and serve popcorn and snacks while the kids enjoy a favorite movie under the stars.\n4. DIY Crafts Party: Arrange a craft party where kids can unleash their creativity. Provide a variety of craft supplies like beads, paints, and fabrics, and let them create their own unique masterpieces to take home as party favors.\n5. Sports Olympics: Host a mini Olympics event with various sports and games. Set up different stations for activities like sack races, relay races, basketball shooting, and obstacle courses. Give out medals or certificates to the participants.\n6. Cooking Party: Have a cooking-themed party where the kids can prepare their own mini pizzas, cupcakes, or cookies. Provide toppings, frosting, and decorating supplies, and let them get hands-on in the kitchen.\n7. Superhero Training Camp: Create a superhero-themed party where the kids can engage in fun training activities. Set up an obstacle course, have them design their own superhero capes or masks, and organize superhero-themed games and challenges.\n8. Outdoor Adventure: Plan an outdoor adventure party at a local park or nature reserve. Arrange activities like hiking, nature scavenger hunts, or a picnic with games. Encourage exploration and appreciation for the outdoors.\nRemember to tailor the activities to the birthday child's interests and preferences. Have a great celebration!\n### Human: {prompt}\n### Assistant:"

prompt = "How do I mount a tv to drywall safely?"

input_str = template.format(prompt=prompt)
input_ids = tokenizer(input_str, return_tensors="pt").input_ids
outputs = model.generate(input_ids, max_length=1000)
print(tokenizer.batch_decode(outputs[:, input_ids.shape[1]:-1])[0].strip())

Alternatively, you may use FastChat:

python3 -m fastchat.serve.cli --model-path LLM360/AmberChat

AmberChat Finetuning Details

DataMix

Hyperparameters

Using Quantized Models with Ollama

Please follow these steps to use a quantized version of AmberChat on your personal computer or laptop:

1. First, install Ollama by following the instructions provided here. Next, download a quantized model checkpoint (such as amberchat.Q8_0.gguf for the 8 bit version) from TheBloke/AmberChat-GGUF. Create an Ollama Modelfile locally using the template provided below:

FROM amberchat.Q8_0.gguf

TEMPLATE """{{ .System }}
USER: {{ .Prompt }}
ASSISTANT:
"""
SYSTEM """A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
"""
PARAMETER stop "USER:"
PARAMETER stop "ASSISTANT:"
PARAMETER repeat_last_n   0
PARAMETER num_ctx         2048
PARAMETER seed            0
PARAMETER num_predict    -1

Ensure that the FROM directive points to the downloaded checkpoint file.

2. Now, you can proceed to build the model by running:

ollama create amberchat -f Modelfile

3. To run the model from the command line, execute the following:

ollama run amberchat

You need to build the model once and can just run it afterwards.

Citation

BibTeX:

@misc{liu2023llm360,
      title={LLM360: Towards Fully Transparent Open-Source LLMs}, 
      author={Zhengzhong Liu and Aurick Qiao and Willie Neiswanger and Hongyi Wang and Bowen Tan and Tianhua Tao and Junbo Li and Yuqi Wang and Suqi Sun and Omkar Pangarkar and Richard Fan and Yi Gu and Victor Miller and Yonghao Zhuang and Guowei He and Haonan Li and Fajri Koto and Liping Tang and Nikhil Ranjan and Zhiqiang Shen and Xuguang Ren and Roberto Iriondo and Cun Mu and Zhiting Hu and Mark Schulze and Preslav Nakov and Tim Baldwin and Eric P. Xing},
      year={2023},
      eprint={2312.06550},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}