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RichardErkhov/
LLM360_-_AmberChat-gguf

Name: LLM360_-_AmberChat-gguf
Availability: InStock

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Quantization made by Richard Erkhov.

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AmberChat - GGUF

Model creator: https://huggingface.co/LLM360/
Original model: https://huggingface.co/LLM360/AmberChat/

Name	Quant method	Size
AmberChat.Q2_K.gguf	Q2_K	2.36GB
AmberChat.IQ3_XS.gguf	IQ3_XS	2.6GB
AmberChat.IQ3_S.gguf	IQ3_S	2.75GB
AmberChat.Q3_K_S.gguf	Q3_K_S	2.75GB
AmberChat.IQ3_M.gguf	IQ3_M	2.9GB
AmberChat.Q3_K.gguf	Q3_K	3.07GB
AmberChat.Q3_K_M.gguf	Q3_K_M	3.07GB
AmberChat.Q3_K_L.gguf	Q3_K_L	3.35GB
AmberChat.IQ4_XS.gguf	IQ4_XS	3.4GB
AmberChat.Q4_0.gguf	Q4_0	3.56GB
AmberChat.IQ4_NL.gguf	IQ4_NL	1.23GB
AmberChat.Q4_K_S.gguf	Q4_K_S	3.59GB
AmberChat.Q4_K.gguf	Q4_K	3.8GB
AmberChat.Q4_K_M.gguf	Q4_K_M	3.8GB
AmberChat.Q4_1.gguf	Q4_1	3.95GB
AmberChat.Q5_0.gguf	Q5_0	4.33GB
AmberChat.Q5_K_S.gguf	Q5_K_S	1.64GB
AmberChat.Q5_K.gguf	Q5_K	4.45GB
AmberChat.Q5_K_M.gguf	Q5_K_M	4.45GB
AmberChat.Q5_1.gguf	Q5_1	4.72GB
AmberChat.Q6_K.gguf	Q6_K	5.15GB
AmberChat.Q8_0.gguf	Q8_0	6.67GB

Original model description:

license: apache-2.0 datasets:

WizardLM/WizardLM_evol_instruct_V2_196k
icybee/share_gpt_90k_v1 language:
en widget:
- example_title: "example 1" text: "How do I mount a tv to drywall safely?" output: text: "Mounting a TV to drywall safely is crucial to ensure the longevity of your TV and the wall. Here's a step-by-step guide on how to do it safely:\n 1. Choose the right mount: Choose a mount that is compatible with your TV and the wall. Make sure the mount is rated for drywall and not just wood or concrete.\n 2. Prepare the wall: Before mounting the TV, make sure the wall is clean and free from any debris or paint. Use a stud finder to locate the studs in the wall.\n 3. Install the mount: Follow the manufacturer's instructions for mounting the TV to the wall. Use appropriate hardware and tools to secure the mount to the wall.\n 4. Level the TV: Level the TV on the mount using a spirit level or a leveling kit provided by the mount manufacturer.\n 5. Attach the TV to the mount: Attach the TV to the mount using the appropriate hardware and tools. Tighten the bolts and screws to ensure the TV is securely attached.\n 6. Connect the cables: Connect the TV cables to the appropriate ports on the back of the TV and the mount.\n 7. Test the mount: Test the mount to ensure it's secure and stable. Adjust the mount as needed to ensure the TV is level and secure.\n Mounting a TV to drywall safely is crucial to avoid damaging the wall or the TV. Follow these steps carefully and use appropriate tools and hardware to ensure a secure and stable installation."
- example_title: "example 2" text: "Happy is to sad as calm is to _." output: text: "The adjective that can be used to describe the opposite of calm is "anxious" or "stressed." So, from happy to sad, we can say that happy is to sad as calm is to anxious or stressed." library_name: transformers pipeline_tag: text-generation tags:
nlp
llm

AmberChat

We present AmberChat, an instruction following model finetuned from LLM360/Amber.

Evaluation

Model	MT-Bench
LLM360/AmberChat	5.428125
LLM360/Amber	2.48750
Falcon-40B-Instruct	5.17
MPT-7B-Chat	5.42
Nous-Hermes-13B	5.51

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:

Loading AmberChat

python

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

Subset	Number of rows	License
WizardLM/WizardLM_evol_instruct_V2_196k	143k
icybee/share_gpt_90k_v1	90k	cc0-1.0
Total	233k

Hyperparameters

Hyperparameter	Value
Total Parameters	6.7B
Hidden Size	4096
Intermediate Size (MLPs)	11008
Number of Attention Heads	32
Number of Hidden Lyaers	32
RMSNorm ɛ	1e^-6
Max Seq Length	2048
Vocab Size	32000

Training Hyperparameter	Value
learning_rate	2e-5
num_train_epochs	3
per_device_train_batch_size	2
gradient_accumulation_steps	16
warmup_ratio	0.04
model_max_length	2048

Using Quantized Models with Ollama

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

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.

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

ollama create amberchat -f Modelfile

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:

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}
}

RichardErkhov/LLM360_-_AmberChat-gguf