---
base_model: TinyLlama/TinyLlama-1.1B-Chat-v0.6
datasets:
- cerebras/SlimPajama-627B
- bigcode/starcoderdata
- OpenAssistant/oasst_top1_2023-08-25
inference: false
language:
- en
license: apache-2.0
model_creator: TinyLlama
model_name: TinyLlama-1.1B-Chat-v0.6
pipeline_tag: text-generation
quantized_by: afrideva
tags:
- gguf
- ggml
- quantized
- q2_k
- q3_k_m
- q4_k_m
- q5_k_m
- q6_k
- q8_0
---
# TinyLlama/TinyLlama-1.1B-Chat-v0.6-GGUF

Quantized GGUF model files for [TinyLlama-1.1B-Chat-v0.6](https://huggingface.co./TinyLlama/TinyLlama-1.1B-Chat-v0.6) from [TinyLlama](https://huggingface.co./TinyLlama)


| Name | Quant method | Size |
| ---- | ---- | ---- |
| [tinyllama-1.1b-chat-v0.6.q2_k.gguf](https://huggingface.co./afrideva/TinyLlama-1.1B-Chat-v0.6-GGUF/resolve/main/tinyllama-1.1b-chat-v0.6.q2_k.gguf) | q2_k | 482.14 MB  |
| [tinyllama-1.1b-chat-v0.6.q3_k_m.gguf](https://huggingface.co./afrideva/TinyLlama-1.1B-Chat-v0.6-GGUF/resolve/main/tinyllama-1.1b-chat-v0.6.q3_k_m.gguf) | q3_k_m | 549.85 MB  |
| [tinyllama-1.1b-chat-v0.6.q4_k_m.gguf](https://huggingface.co./afrideva/TinyLlama-1.1B-Chat-v0.6-GGUF/resolve/main/tinyllama-1.1b-chat-v0.6.q4_k_m.gguf) | q4_k_m | 667.81 MB  |
| [tinyllama-1.1b-chat-v0.6.q5_k_m.gguf](https://huggingface.co./afrideva/TinyLlama-1.1B-Chat-v0.6-GGUF/resolve/main/tinyllama-1.1b-chat-v0.6.q5_k_m.gguf) | q5_k_m | 782.04 MB  |
| [tinyllama-1.1b-chat-v0.6.q6_k.gguf](https://huggingface.co./afrideva/TinyLlama-1.1B-Chat-v0.6-GGUF/resolve/main/tinyllama-1.1b-chat-v0.6.q6_k.gguf) | q6_k | 903.41 MB  |
| [tinyllama-1.1b-chat-v0.6.q8_0.gguf](https://huggingface.co./afrideva/TinyLlama-1.1B-Chat-v0.6-GGUF/resolve/main/tinyllama-1.1b-chat-v0.6.q8_0.gguf) | q8_0 | 1.17 GB  |



## Original Model Card:
<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-955k-2T](https://huggingface.co./TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T). **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-v0.6", 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|>
# ...
```