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---
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:\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](https://huggingface.co/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:**
- [Metrics](https://github.com/LLM360/Analysis360)
- [Fully processed Amber pretraining data](https://huggingface.co/datasets/LLM360/AmberDatasets)
# Loading AmberChat
```python
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:**
```bibtex
@article{xxx,
title={XXX},
author={XXX},
journal={XXX},
year={2023}
}
``` |