Update README.md

README.md

@@ -9,6 +9,9 @@ tags:
 - unsloth
 - qwen2
 - trl
+datasets:
+- lightontech/tech-viet-translation
+pipeline_tag: text-generation
 ---
 
 # Uploaded  model
@@ -20,3 +23,53 @@ tags:
 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
+
+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"
+```
+
+```
+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)
+```