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license: apache-2.0
base_model: TinyLlama/TinyLlama-1.1B-Chat-v0.6
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: TinyLlama-v2ray
results: []
datasets:
- theminji/v2ray
library_name: transformers
widget:
- text: "<|im_start|>user\nWho are you?<|im_end|>\n<|im_start|>assistant"
example_title: "First Example"
- text: "<|im_start|>user\nhow much do you goon?<|im_end|>\n<|im_start|>assistant"
example_title: "Second Example"
---
# TinyLlama-v2ray
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v0.6](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v0.6) on the [theminji/v2ray](https://huggingface.co/datasets/theminji/v2ray) dataset.
## Model description
Prompt format is as follows:
```py
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
The model is intended to mimic the behavior of v2ray, so results will most likely be nonsensical or gibberish.
## Example Usage
```py
import torch
from transformers import pipeline, AutoTokenizer
import re
tokenizer = AutoTokenizer.from_pretrained("theminji/TinyLlama-v2ray")
pipe = pipeline("text-generation", model="theminji/TinyLlama-v2ray", torch_dtype=torch.bfloat16, device_map="auto")
def formatted_prompt(prompt)-> str:
return f"<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant"
def extract_text(text):
pattern = r'v2ray\n(.*?)(?=<\|im_end\|>)'
match = re.search(pattern, text, re.DOTALL)
if match:
return f"Output: {match.group(1)}"
else:
return "No match found"
prompt = 'what are your thoughts on ccp'
outputs = pipe(formatted_prompt(prompt), max_new_tokens=50, do_sample=True, temperature=0.9)
if outputs and "generated_text" in outputs[0]:
text = extract_text(outputs[0]["generated_text"])
print(f"Prompt: {prompt}")
print("")
print(text)
else:
print("No output or unexpected structure")
#Prompt: what are ur thoughts on ccp
#
#Output: <Re: insaneness> you are a ccp
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 1000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0 |