Update README.md (#8)

- Update README.md (aa1dfabd1e33f174226b803a80e3d091008ee83d)


Co-authored-by: Pakawat Phasook <[email protected]>

README.md

@@ -18,32 +18,12 @@ tags:
 - medical
 - text-generation-inference
 ---
-# OpenThaiLLM-: Thai & China Large Language Model (Instruct)
-**OpenThaiLLM-DoodNiLT-Instruct** is an 7 billion parameter instruct model designed for Thai 🇹🇭 & China 🇨🇳 language. 
-It demonstrates competitive performance with GPT-3.5-turbo and llama-3-typhoon-v1.5-8b-instruct, and is optimized for application use cases, Retrieval-Augmented Generation (RAG), 
+# OpenThaiLLM-Prebuilt-7B: Thai & China & English Large Language Model
+**OpenThaiLLM-Prebuilt-7B** is an 7 billion parameter instruct model designed for Thai 🇹🇭 & China 🇨🇳 language. 
+It demonstrates competitive performance with llama-3-typhoon-v1.5-8b-instruct, and is optimized for application use cases, Retrieval-Augmented Generation (RAG), 
 constrained generation, and reasoning tasks.is a Thai 🇹🇭 & China 🇨🇳 large language model with 7 billion parameters, and it is based on Qwen2-7B.
-## Introduction
 
-Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:
-
-- Significantly **more knowledge** and has greatly improved capabilities in **coding** and **mathematics**, thanks to our specialized expert models in these domains.
-- Significant improvements in **instruction following**, **generating long texts** (over 8K tokens), **understanding structured data** (e.g, tables), and **generating structured outputs** especially JSON. **More resilient to the diversity of system prompts**, enhancing role-play implementation and condition-setting for chatbots.
-- **Long-context Support** up to 128K tokens and can generate up to 8K tokens.
-- **Multilingual support** for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. 
-
-**This repo contains the base 7B Qwen2.5 model**, which has the following features:
-- Type: Causal Language Models
-- Training Stage: Pretraining
-- Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
-- Number of Parameters: 7.61B
-- Number of Paramaters (Non-Embedding): 6.53B
-- Number of Layers: 28
-- Number of Attention Heads (GQA): 28 for Q and 4 for KV
-- Context Length: 131,072 tokens
-
-**We do not recommend using base language models for conversations.** Instead, you can apply post-training, e.g., SFT, RLHF, continued pretraining, etc., on this model.
-
-For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5/), [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/).
+For release notes, please see our [blog](https://medium.com/@superkingbasskb/openthaillm-prebuilt-release-f1b0e22be6a5).
 
 ## Requirements
 
@@ -61,38 +41,6 @@ We pretrained the models with a large amount of data, and we post-trained the mo
 
 Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
 
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-device = "cuda" # the device to load the model onto
-
-model = AutoModelForCausalLM.from_pretrained(
-    "nectec/OpenThaiLLM-DoodNiLT-V1.0.0-Beta-7B-Instruct",
-    torch_dtype="auto",
-    device_map="auto"
-)
-tokenizer = AutoTokenizer.from_pretrained("nectec/OpenThaiLLM-DoodNiLT-V1.0.0-Beta-7B-Instruct")
-
-prompt = "บริษัท A มีต้นทุนคงที่ 100,000 บาท และต้นทุนผันแปรต่อหน่วย 50 บาท ขายสินค้าได้ในราคา 150 บาทต่อหน่วย ต้องขายสินค้าอย่างน้อยกี่หน่วยเพื่อให้ถึงจุดคุ้มทุน?"
-messages = [
-    {"role": "system", "content": "คุณคือ DoodNiLT Assistant จงตอบคำถามอธิบายเป็นภาษาไทย"},
-    {"role": "user", "content": prompt}
-]
-text = tokenizer.apply_chat_template(
-    messages,
-    tokenize=False,
-    add_generation_prompt=True
-)
-model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
-
-generated_ids = model.generate(
-    model_inputs.input_ids,
-    max_new_tokens=4096,
-    repetition_penalty=1.2
-)
-response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
-print(response)
-```
-
 ## Evaluation Performance
 | Model | ONET | IC | TGAT | TPAT-1 | A-Level | Average (ThaiExam) | M3Exam (1 shot) | MMLU |
 | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |