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base_model: SeaLLMs/SeaLLM3-7B-Chat
language:
- en
- vi
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
datasets:
- lightontech/tech-viet-translation
pipeline_tag: text-generation
---
# Uploaded model
- **Developed by:** lightontech
- **License:** apache-2.0
- **Finetuned from model :** SeaLLMs/SeaLLM3-7B-Chat
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
To use GGUF format for Llama.cpp or running in LM Studio, Jan and other local software, please refer to [lightontech/SeaLightSum3_GGUF](https://huggingface.co/lightontech/SeaLightSum3_GGUF)
# How to use
For faster startup, checkout the [Example notebook here](https://colab.research.google.com/drive/1h6NyOBCzSYrx-nBoRA1X40loIe2oTioA?usp=sharing)
## Install unsloth
This sample use unsloth for colab, you may switch to unsloth only if you want
```
pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
pip install --no-deps "xformers<0.0.27" "trl<0.9.0" peft accelerate bitsandbytes
```
## Run inference
```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:
{}"""
if True:
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "lightontech/SeaLightSum3-Adapter", # YOUR MODEL YOU USED FOR TRAINING
max_seq_length = max_seq_length,
dtype = dtype,
load_in_4bit = load_in_4bit,
)
FastLanguageModel.for_inference(model) # Unsloth has 2x faster inference!
# alpaca_prompt = You MUST copy from above!
FastLanguageModel.for_inference(model) # Unsloth has 2x faster inference!
inputs = tokenizer(
[
alpaca_prompt.format(
"Dịch đoạn văn sau sang tiếng Việt:\nOnce you have trained a model using either the SFTTrainer, PPOTrainer, or DPOTrainer, you will have a fine-tuned model that can be used for text generation. In this section, we’ll walk through the process of loading the fine-tuned model and generating text. If you need to run an inference server with the trained model, you can explore libraries such as text-generation-inference.", # instruction
"", # input
"", # output - leave this blank for generation!
)
], return_tensors = "pt").to("cuda")
from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1000)
``` |