Text Generation
Transformers
English
llama
nlp
llm
Inference Endpoints
Edit model card

AmberChat

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

Model Description

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

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

Evaluation

Model MT-Bench
LLM360/Amber 359 2.48750
LLM360/AmberChat 5.428125

Citation

BibTeX:

@article{xxx,
  title={XXX},
  author={XXX},
  journal={XXX},
  year={2023}
}
Downloads last month
7
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Datasets used to train LoneStriker/AmberChat-5.0bpw-h6-exl2-2