README.md

---
license: apache-2.0
datasets:
- cerebras/SlimPajama-627B
- bigcode/starcoderdata
- HuggingFaceH4/ultrachat_200k
- HuggingFaceH4/ultrafeedback_binarized
language:
- en
widget:
- text: "<|system|>\nYou are a chatbot who can help code!</s>\n<|user|>\nWrite me a function to calculate the first 10 digits of the fibonacci sequence in Python and print it out to the CLI.</s>\n<|assistant|>\n"
---
<div align="center">

# TinyLlama-1.1B ---My personal Test update
</div>

|    Tasks    |Version|Filter|n-shot| Metric |Value |   |Stderr|
|-------------|-------|------|-----:|--------|-----:|---|-----:|
|arc_challenge|Yaml   |none  |     0|acc     |0.2619|±  |0.0128|
|             |       |none  |     0|acc_norm|0.2892|±  |0.0133|
|arc_easy     |Yaml   |none  |     0|acc     |0.4777|±  |0.0102|
|             |       |none  |     0|acc_norm|0.4461|±  |0.0102|
|boolq        |Yaml   |none  |     0|acc     |0.6297|±  |0.0084|
|hellaswag    |Yaml   |none  |     0|acc     |0.3934|±  |0.0049|
|             |       |none  |     0|acc_norm|0.4930|±  |0.0050|
|openbookqa   |Yaml   |none  |     0|acc     |0.2120|±  |0.0183|
|             |       |none  |     0|acc_norm|0.3260|±  |0.0210|
|piqa         |Yaml   |none  |     0|acc     |0.6915|±  |0.0108|
|             |       |none  |     0|acc_norm|0.6877|±  |0.0108|
|winogrande   |Yaml   |none  |     0|acc     |0.5714|±  |0.0139|


Llamafactory EVAL 


!CUDA_VISIBLE_DEVICES=0 python src/evaluate.py \
    --model_name_or_path Deathsquad10/TinyLlama-Remix \
    --template vanilla \
    --task mmlu \
    --split test \
    --lang en \
    --n_shot 5 \
    --use_unsloth \
    --batch_size 1
    
    
           Average: 26.29
           STEM: 27.10
           Social Sciences: 25.48
           Humanities: 25.62
           Other: 27.26

!CUDA_VISIBLE_DEVICES=0 python src/evaluate.py \
    --model_name_or_path Deathsquad10/TinyLlama-Remix \
    --template vanilla \
    --task cmmlu \
    --split test \
    --lang en \
    --n_shot 5 \
    --use_unsloth \
    --batch_size 2


          Average: 24.98
          STEM: 25.52
          Social Sciences: 24.70
          Humanities: 24.59
          Other: 25.19
https://github.com/jzhang38/TinyLlama

The TinyLlama project aims to **pretrain** a **1.1B Llama model on 3 trillion tokens**. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01. 


We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.

#### This Model
This is the chat model finetuned on top of [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T). **We follow [HF's Zephyr](https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha/edit/main/README.md)'s training recipe.** The model was " initially fine-tuned on a variant of the [`UltraChat`](https://huggingface.co/datasets/stingning/ultrachat) dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. 
We then further aligned the model with [🤗 TRL's](https://github.com/huggingface/trl) `DPOTrainer` on the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset, which contain 64k prompts and model completions that are ranked by GPT-4." 


#### How to use
You will need the transformers>=4.34
Do check the [TinyLlama](https://github.com/jzhang38/TinyLlama) github page for more information.

```python
# Install transformers from source - only needed for versions <= v4.34
# pip install git+https://github.com/huggingface/transformers.git
# pip install accelerate
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="TinyLlama/TinyLlama-1.1B-Chat-v1.0", torch_dtype=torch.bfloat16, device_map="auto")
# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
    {
        "role": "system",
        "content": "You are a friendly chatbot who always responds in the style of a pirate",
    },
    {"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
# <|system|>
# You are a friendly chatbot who always responds in the style of a pirate.</s>
# <|user|>
# How many helicopters can a human eat in one sitting?</s>
# <|assistant|>
# ...
```