---
library_name: transformers
base_model: unsloth/tinyllama-bnb-4bit
license: mit
datasets:
- yahma/alpaca-cleaned
language:
- en
pipeline_tag: text-generation
tags:
- Instruct
- TinyLlama
---

# Steps to try the model:

### prompt Template 
```python
alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""
```
### load the model

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("damerajee/tinyllama-sft-small-v2")
model = AutoModelForCausalLM.from_pretrained("damerajee/tinyllama-sft-small-v2")
```
### Inference

```python
inputs = tokenizer(
[
    alpaca_prompt.format(
        "best places to visit in india", # instruction
        "", # input
        "", # output
    )
]*1, return_tensors = "pt")

outputs = model.generate(**inputs, max_new_tokens = 128, use_cache = True)
tokenizer.batch_decode(outputs)
```



# Model Information
The base model [unsloth/tinyllama-bnb-4bit](https://huggingface.co/unsloth/tinyllama-bnb-4bit) was Instruct finetuned using [Unsloth](https://github.com/unslothai/unsloth)

# Model Limitations
The model was trained on a very small dataset so it might not be as good ,will be training on larger dataset soon
# Training Details
The model was trained for 1 epoch on a free goggle colab which took about 1 hour and 30 mins approximately