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TinyLlama_-_TinyLlama-1.1B-intermediate-step-240k-503b-gguf

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TinyLlama_-_TinyLlama-1.1B-intermediate-step-240k-503b-gguf
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Quantization made by Richard Erkhov.

[Github](https://github.com/RichardErkhov)

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TinyLlama-1.1B-intermediate-step-240k-503b - GGUF
- Model creator: https://huggingface.co/TinyLlama/
- Original model: https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-240k-503b/


| Name | Quant method | Size |
| ---- | ---- | ---- |
| [TinyLlama-1.1B-intermediate-step-240k-503b.Q2_K.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-240k-503b-gguf/blob/main/TinyLlama-1.1B-intermediate-step-240k-503b.Q2_K.gguf) | Q2_K | 0.4GB |
| [TinyLlama-1.1B-intermediate-step-240k-503b.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-240k-503b-gguf/blob/main/TinyLlama-1.1B-intermediate-step-240k-503b.IQ3_XS.gguf) | IQ3_XS | 0.44GB |
| [TinyLlama-1.1B-intermediate-step-240k-503b.IQ3_S.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-240k-503b-gguf/blob/main/TinyLlama-1.1B-intermediate-step-240k-503b.IQ3_S.gguf) | IQ3_S | 0.47GB |
| [TinyLlama-1.1B-intermediate-step-240k-503b.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-240k-503b-gguf/blob/main/TinyLlama-1.1B-intermediate-step-240k-503b.Q3_K_S.gguf) | Q3_K_S | 0.47GB |
| [TinyLlama-1.1B-intermediate-step-240k-503b.IQ3_M.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-240k-503b-gguf/blob/main/TinyLlama-1.1B-intermediate-step-240k-503b.IQ3_M.gguf) | IQ3_M | 0.48GB |
| [TinyLlama-1.1B-intermediate-step-240k-503b.Q3_K.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-240k-503b-gguf/blob/main/TinyLlama-1.1B-intermediate-step-240k-503b.Q3_K.gguf) | Q3_K | 0.51GB |
| [TinyLlama-1.1B-intermediate-step-240k-503b.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-240k-503b-gguf/blob/main/TinyLlama-1.1B-intermediate-step-240k-503b.Q3_K_M.gguf) | Q3_K_M | 0.51GB |
| [TinyLlama-1.1B-intermediate-step-240k-503b.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-240k-503b-gguf/blob/main/TinyLlama-1.1B-intermediate-step-240k-503b.Q3_K_L.gguf) | Q3_K_L | 0.55GB |
| [TinyLlama-1.1B-intermediate-step-240k-503b.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-240k-503b-gguf/blob/main/TinyLlama-1.1B-intermediate-step-240k-503b.IQ4_XS.gguf) | IQ4_XS | 0.57GB |
| [TinyLlama-1.1B-intermediate-step-240k-503b.Q4_0.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-240k-503b-gguf/blob/main/TinyLlama-1.1B-intermediate-step-240k-503b.Q4_0.gguf) | Q4_0 | 0.59GB |
| [TinyLlama-1.1B-intermediate-step-240k-503b.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-240k-503b-gguf/blob/main/TinyLlama-1.1B-intermediate-step-240k-503b.IQ4_NL.gguf) | IQ4_NL | 0.6GB |
| [TinyLlama-1.1B-intermediate-step-240k-503b.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-240k-503b-gguf/blob/main/TinyLlama-1.1B-intermediate-step-240k-503b.Q4_K_S.gguf) | Q4_K_S | 0.6GB |
| [TinyLlama-1.1B-intermediate-step-240k-503b.Q4_K.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-240k-503b-gguf/blob/main/TinyLlama-1.1B-intermediate-step-240k-503b.Q4_K.gguf) | Q4_K | 0.62GB |
| [TinyLlama-1.1B-intermediate-step-240k-503b.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-240k-503b-gguf/blob/main/TinyLlama-1.1B-intermediate-step-240k-503b.Q4_K_M.gguf) | Q4_K_M | 0.62GB |
| [TinyLlama-1.1B-intermediate-step-240k-503b.Q4_1.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-240k-503b-gguf/blob/main/TinyLlama-1.1B-intermediate-step-240k-503b.Q4_1.gguf) | Q4_1 | 0.65GB |
| [TinyLlama-1.1B-intermediate-step-240k-503b.Q5_0.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-240k-503b-gguf/blob/main/TinyLlama-1.1B-intermediate-step-240k-503b.Q5_0.gguf) | Q5_0 | 0.71GB |
| [TinyLlama-1.1B-intermediate-step-240k-503b.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-240k-503b-gguf/blob/main/TinyLlama-1.1B-intermediate-step-240k-503b.Q5_K_S.gguf) | Q5_K_S | 0.71GB |
| [TinyLlama-1.1B-intermediate-step-240k-503b.Q5_K.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-240k-503b-gguf/blob/main/TinyLlama-1.1B-intermediate-step-240k-503b.Q5_K.gguf) | Q5_K | 0.73GB |
| [TinyLlama-1.1B-intermediate-step-240k-503b.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-240k-503b-gguf/blob/main/TinyLlama-1.1B-intermediate-step-240k-503b.Q5_K_M.gguf) | Q5_K_M | 0.73GB |
| [TinyLlama-1.1B-intermediate-step-240k-503b.Q5_1.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-240k-503b-gguf/blob/main/TinyLlama-1.1B-intermediate-step-240k-503b.Q5_1.gguf) | Q5_1 | 0.77GB |
| [TinyLlama-1.1B-intermediate-step-240k-503b.Q6_K.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-240k-503b-gguf/blob/main/TinyLlama-1.1B-intermediate-step-240k-503b.Q6_K.gguf) | Q6_K | 0.84GB |
| [TinyLlama-1.1B-intermediate-step-240k-503b.Q8_0.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-240k-503b-gguf/blob/main/TinyLlama-1.1B-intermediate-step-240k-503b.Q8_0.gguf) | Q8_0 | 1.09GB |




Original model description:
---
license: apache-2.0
datasets:
- cerebras/SlimPajama-627B
- bigcode/starcoderdata
language:
- en
---
<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 240K steps and 503B tokens. **We suggest you not use this directly for inference.** The [chat model](https://huggingface.co/PY007/TinyLlama-1.1B-Chat-v0.1) is always preferred **


#### 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 = "PY007/TinyLlama-1.1B-intermediate-step-240k-503b"
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']}")
```