---
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T
datasets:
- cerebras/SlimPajama-627B
- bigcode/starcoderdata
inference: false
language:
- en
license: apache-2.0
model_creator: TinyLlama
model_name: TinyLlama-1.1B-intermediate-step-955k-token-2T
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-intermediate-step-955k-token-2T-GGUF

Quantized GGUF model files for [TinyLlama-1.1B-intermediate-step-955k-token-2T](https://huggingface.co./TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T) from [TinyLlama](https://huggingface.co./TinyLlama)


| Name | Quant method | Size |
| ---- | ---- | ---- |
| [tinyllama-1.1b-intermediate-step-955k-token-2t.q2_k.gguf](https://huggingface.co./afrideva/TinyLlama-1.1B-intermediate-step-955k-token-2T-GGUF/resolve/main/tinyllama-1.1b-intermediate-step-955k-token-2t.q2_k.gguf) | q2_k | 482.14 MB  |
| [tinyllama-1.1b-intermediate-step-955k-token-2t.q3_k_m.gguf](https://huggingface.co./afrideva/TinyLlama-1.1B-intermediate-step-955k-token-2T-GGUF/resolve/main/tinyllama-1.1b-intermediate-step-955k-token-2t.q3_k_m.gguf) | q3_k_m | 549.85 MB  |
| [tinyllama-1.1b-intermediate-step-955k-token-2t.q4_k_m.gguf](https://huggingface.co./afrideva/TinyLlama-1.1B-intermediate-step-955k-token-2T-GGUF/resolve/main/tinyllama-1.1b-intermediate-step-955k-token-2t.q4_k_m.gguf) | q4_k_m | 667.81 MB  |
| [tinyllama-1.1b-intermediate-step-955k-token-2t.q5_k_m.gguf](https://huggingface.co./afrideva/TinyLlama-1.1B-intermediate-step-955k-token-2T-GGUF/resolve/main/tinyllama-1.1b-intermediate-step-955k-token-2t.q5_k_m.gguf) | q5_k_m | 782.04 MB  |
| [tinyllama-1.1b-intermediate-step-955k-token-2t.q6_k.gguf](https://huggingface.co./afrideva/TinyLlama-1.1B-intermediate-step-955k-token-2T-GGUF/resolve/main/tinyllama-1.1b-intermediate-step-955k-token-2t.q6_k.gguf) | q6_k | 903.41 MB  |
| [tinyllama-1.1b-intermediate-step-955k-token-2t.q8_0.gguf](https://huggingface.co./afrideva/TinyLlama-1.1B-intermediate-step-955k-token-2T-GGUF/resolve/main/tinyllama-1.1b-intermediate-step-955k-token-2t.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. 

<div align="center">
  <img src="./TinyLlama_logo.png" width="300"/>
</div>

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 an intermediate checkpoint with 995K steps and 2003B tokens.

#### Releases Schedule
We will be rolling out intermediate checkpoints following the below schedule. We also include some baseline models for comparison.

| Date       | HF Checkpoint                                   | Tokens | Step | HellaSwag Acc_norm |
|------------|-------------------------------------------------|--------|------|---------------------|
| Baseline   | [StableLM-Alpha-3B](https://huggingface.co./stabilityai/stablelm-base-alpha-3b)| 800B   | --   |  38.31            |
| Baseline   | [Pythia-1B-intermediate-step-50k-105b](https://huggingface.co./EleutherAI/pythia-1b/tree/step50000)             | 105B   | 50k   |  42.04            |
| Baseline   | [Pythia-1B](https://huggingface.co./EleutherAI/pythia-1b)             | 300B   | 143k   |  47.16            |
| 2023-09-04 | [TinyLlama-1.1B-intermediate-step-50k-105b](https://huggingface.co./PY007/TinyLlama-1.1B-step-50K-105b) | 105B   | 50k   |  43.50               |
| 2023-09-16 | --                                             | 500B   | --   |  --               |
| 2023-10-01 | --                                             | 1T     | --   |  --               |
| 2023-10-16 | --                                             | 1.5T   | --   |  --               |
| 2023-10-31 | --                                             | 2T     | --   |  --               |
| 2023-11-15 | --                                             | 2.5T   | --   |  --               |
| 2023-12-01 | --                                             | 3T     | --   |  --               |

#### How to use
You will need the transformers>=4.31
Do check the [TinyLlama](https://github.com/jzhang38/TinyLlama) github page for more information.
```
from transformers import AutoTokenizer
import transformers 
import torch
model = "TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

sequences = pipeline(
    '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.',
    do_sample=True,
    top_k=10,
    num_return_sequences=1,
    repetition_penalty=1.5,
    eos_token_id=tokenizer.eos_token_id,
    max_length=500,
)
for seq in sequences:
    print(f"Result: {seq['generated_text']}")
```