初始化项目，由ModelHub XC社区提供模型

Model: wannaphong/han-llm-7b-v3
Source: Original Platform

README.md

@@ -0,0 +1,71 @@
+---
+library_name: transformers
+license: apache-2.0
+datasets:
+- pythainlp/han-instruct-dataset-v2.0
+language:
+- th
+pipeline_tag: text-generation
+---
+
+# Model Card for Han LLM 7B v3
+
+Han LLM 7B v3 is a model that trained by han-instruct-dataset v2.0 and more. The model are working with Thai.
+
+Base model: [scb10x/typhoon-7b](https://huggingface.co/scb10x/typhoon-7b)
+
+[Google colab: Demo Han LLM 7B v3](https://colab.research.google.com/drive/1eC3dIWjBgM2v_UyCopMLawvqqcnQFvmI?usp=sharing)
+
+
+
+Thank you kaggle for free gpu!
+
+## Model Details
+
+### Model Description
+
+The model was trained by LoRA.
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** Wannaphong Phatthiyaphaibun
+- **Model type:** text-generation
+- **Language(s) (NLP):** Thai
+- **License:** apache-2.0
+- **Finetuned from model:** [scb10x/typhoon-7b](https://huggingface.co/scb10x/typhoon-7b)
+
+## Uses
+
+Thai users
+
+### Out-of-Scope Use
+
+Math, Coding, and other language
+
+
+## Bias, Risks, and Limitations
+
+The model can has a bias from dataset. Use at your own risks!
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+**Example**
+
+
+```python
+# !pip install accelerate sentencepiece transformers bitsandbytes
+import torch
+from transformers import pipeline
+
+pipe = pipeline("text-generation", model="wannaphong/han-llm-7b-v3", torch_dtype=torch.bfloat16, device_map="auto")
+
+# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
+messages = [
+    {"role": "user", "content": "แมวคืออะไร"},
+]
+prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+outputs = pipe(prompt, max_new_tokens=300, do_sample=True, temperature=0.9, top_k=50, top_p=0.95, no_repeat_ngram_size=2,typical_p=1.)
+print(outputs[0]["generated_text"])
+```