# TinyLlama-1.1B-Chat-v1.0
---


## README([From Huggingface](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0))


<div align="center">

# TinyLlama-1.1B
</div>

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)'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 paddle
from paddlenlp.transformers import pipeline

pipe = pipeline("text-generation", model="TinyLlama/TinyLlama-1.1B-Chat-v1.0", dtype=paddle.bfloat16, )

# 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|>
# ...
```



## Model Files

- [README.md](https://paddlenlp.bj.bcebos.com/models/community/TinyLlama/TinyLlama-1.1B-Chat-v1.0/README.md) (3.1 KB)

- [config.json](https://paddlenlp.bj.bcebos.com/models/community/TinyLlama/TinyLlama-1.1B-Chat-v1.0/config.json) (602.0 B)

- [eval_results.json](https://paddlenlp.bj.bcebos.com/models/community/TinyLlama/TinyLlama-1.1B-Chat-v1.0/eval_results.json) (566.0 B)

- [generation_config.json](https://paddlenlp.bj.bcebos.com/models/community/TinyLlama/TinyLlama-1.1B-Chat-v1.0/generation_config.json) (124.0 B)

- [model.safetensors](https://paddlenlp.bj.bcebos.com/models/community/TinyLlama/TinyLlama-1.1B-Chat-v1.0/model.safetensors) (2.0 GB)

- [special_tokens_map.json](https://paddlenlp.bj.bcebos.com/models/community/TinyLlama/TinyLlama-1.1B-Chat-v1.0/special_tokens_map.json) (551.0 B)

- [tokenizer.json](https://paddlenlp.bj.bcebos.com/models/community/TinyLlama/TinyLlama-1.1B-Chat-v1.0/tokenizer.json) (1.8 MB)

- [tokenizer.model](https://paddlenlp.bj.bcebos.com/models/community/TinyLlama/TinyLlama-1.1B-Chat-v1.0/tokenizer.model) (488.0 KB)

- [tokenizer_config.json](https://paddlenlp.bj.bcebos.com/models/community/TinyLlama/TinyLlama-1.1B-Chat-v1.0/tokenizer_config.json) (1.3 KB)


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