README.md

---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- nlp
- llm
datasets:
- WizardLM/WizardLM_evol_instruct_V2_196k
- icybee/share_gpt_90k_v1
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.
pipeline_tag: text-generation
model-index:
- name: AmberChat
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 42.92
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=LLM360/AmberChat
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 74.01
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=LLM360/AmberChat
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 38.75
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=LLM360/AmberChat
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 41.18
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=LLM360/AmberChat
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 66.61
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=LLM360/AmberChat
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 5.53
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=LLM360/AmberChat
      name: Open LLM Leaderboard
---
# 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)
  - [Finetuning Code](https://github.com/LLM360/amber-train/tree/main/finetune/amberchat)


# 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](https://github.com/lm-sys/FastChat):
```bash
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/AmberChat** | **5.428125** |
| [LLM360/Amber](https://huggingface.co/LLM360/Amber) | 2.48750 |
| [Falcon-40B-Instruct](https://huggingface.co/tiiuae/falcon-40b-instruct) | 5.17 |
| [MPT-7B-Chat](https://huggingface.co/mosaicml/mpt-7b-chat) | 5.42 |
| [Nous-Hermes-13B](https://huggingface.co/NousResearch/Nous-Hermes-13b) | 5.51 |

# 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](https://github.com/jmorganca/ollama/tree/main?tab=readme-ov-file#ollama). Next, download a quantized model checkpoint (such as [amberchat.Q8_0.gguf](https://huggingface.co/TheBloke/AmberChat-GGUF/blob/main/amberchat.Q8_0.gguf) for the 8 bit version) from [TheBloke/AmberChat-GGUF](https://huggingface.co/TheBloke/AmberChat-GGUF/tree/main). 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:
```bash
ollama create amberchat -f Modelfile
```
3. To run the model from the command line, execute the following:
```bash
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}
}
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_LLM360__AmberChat)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |44.84|
|AI2 Reasoning Challenge (25-Shot)|42.92|
|HellaSwag (10-Shot)              |74.01|
|MMLU (5-Shot)                    |38.75|
|TruthfulQA (0-shot)              |41.18|
|Winogrande (5-shot)              |66.61|
|GSM8k (5-shot)                   | 5.53|