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---
base_model: tog/TinyLlama-1.1B-alpaca-chat-v1.5
datasets:
- tatsu-lab/alpaca
inference: false
language:
- en
license: apache-2.0
model_creator: tog
model_name: TinyLlama-1.1B-alpaca-chat-v1.5
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
widget:
- text: '###Instruction:\nWhat is a large language model? Be concise\n\n### Response:\n'
---
# tog/TinyLlama-1.1B-alpaca-chat-v1.5-GGUF
Quantized GGUF model files for [TinyLlama-1.1B-alpaca-chat-v1.5](https://huggingface.co/tog/TinyLlama-1.1B-alpaca-chat-v1.5) from [tog](https://huggingface.co/tog)
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [tinyllama-1.1b-alpaca-chat-v1.5.q2_k.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-alpaca-chat-v1.5-GGUF/resolve/main/tinyllama-1.1b-alpaca-chat-v1.5.q2_k.gguf) | q2_k | 482.14 MB |
| [tinyllama-1.1b-alpaca-chat-v1.5.q3_k_m.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-alpaca-chat-v1.5-GGUF/resolve/main/tinyllama-1.1b-alpaca-chat-v1.5.q3_k_m.gguf) | q3_k_m | 549.85 MB |
| [tinyllama-1.1b-alpaca-chat-v1.5.q4_k_m.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-alpaca-chat-v1.5-GGUF/resolve/main/tinyllama-1.1b-alpaca-chat-v1.5.q4_k_m.gguf) | q4_k_m | 667.81 MB |
| [tinyllama-1.1b-alpaca-chat-v1.5.q5_k_m.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-alpaca-chat-v1.5-GGUF/resolve/main/tinyllama-1.1b-alpaca-chat-v1.5.q5_k_m.gguf) | q5_k_m | 782.04 MB |
| [tinyllama-1.1b-alpaca-chat-v1.5.q6_k.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-alpaca-chat-v1.5-GGUF/resolve/main/tinyllama-1.1b-alpaca-chat-v1.5.q6_k.gguf) | q6_k | 903.41 MB |
| [tinyllama-1.1b-alpaca-chat-v1.5.q8_0.gguf](https://huggingface.co/afrideva/TinyLlama-1.1B-alpaca-chat-v1.5-GGUF/resolve/main/tinyllama-1.1b-alpaca-chat-v1.5.q8_0.gguf) | q8_0 | 1.17 GB |
## Original Model Card:
## This Model
This is the chat model finetuned on top of [PY007/TinyLlama-1.1B-intermediate-step-715k-1.5T](https://huggingface.co/PY007/TinyLlama-1.1B-intermediate-step-715k-1.5T). The dataset used is [tatsu-lab/stanford_alpaca](https://github.com/tatsu-lab/stanford_alpaca).
Below is an instruction that describes a task. Write a response that appropriately completes the request.
```
### Instruction:
{instruction}
### Response:
```
You can use it with the `transformers` library:
```python
from transformers import AutoTokenizer
import transformers
import torch
model = "tog/TinyLlama-1.1B-alpaca-chat-v1.5"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto")
sequences = pipeline(
'###Instruction:\nWhat is a large language model? Be concise.\n\n### Response:\n',
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
max_length=200)
for seq in sequences:
print(f"{seq['generated_text']}")
```
You should get something along those lines:
```
Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.
Result: ###Instruction:
What is a large language model? Be concise.
### Response:
A large language model is a type of natural language understanding model that can learn to accurately recognize and interpret text data by understanding the context of words. Languages used for text understanding are typically trained on a corpus of text data.
``` |