File size: 1,534 Bytes
fda267d 3c6a3e8 fda267d 3c6a3e8 fda267d 3c6a3e8 fda267d 3c6a3e8 fda267d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 |
---
library_name: peft
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 peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM ,AutoTokenizer
config = PeftConfig.from_pretrained("damerajee/Tinyllama-sft-small")
model = AutoModelForCausalLM.from_pretrained("unsloth/tinyllama")
tokenizer=AutoTokenizer.from_pretrained("damerajee/Tinyllama-sft-small")
model = PeftModel.from_pretrained(model, "damerajee/Tinyllama-sft-small")l")
```
### Inference
```python
inputs = tokenizer(
[
alpaca_prompt.format(
"choose ronaldo or messi?", # 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)
# Training Details
The model was trained for 1 epoch on a free goggle colab which took about 1 hour and 30 mins approximately |