metadata
license: apache-2.0
datasets:
- WizardLM/WizardLM_evol_instruct_V2_196k
- icybee/share_gpt_90k_v1
language:
- en
widget:
- example_title: AmberChat
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:
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.
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.
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.
4. Level the TV: Level the TV on the mount using a spirit level or a leveling kit provided by the mount manufacturer.
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.
6. Connect the cables: Connect the TV cables to the appropriate ports on the back of the TV and the mount.
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.
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.
library_name: transformers
pipeline_tag: text-generation
tags:
- nlp
- llm
AmberChat
We present AmberChat, an instruction following model finetuned from LLM360/Amber.
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
from transformers import LlamaTokenizer, LlamaForCausalLM
tokenizer = LlamaTokenizer.from_pretrained("LLM360/AmberChat")
model = LlamaForCausalLM.from_pretrained("LLM360/AmberChat")
input_text = "How old are you?"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
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 |
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}
}