Update README.md

README.md

@@ -1,7 +1,47 @@
 ---
+inference: false
+language: en
+license: llama2
+model_type: llama
+datasets:
+  - mlabonne/CodeLlama-2-20k
+pipeline_tag: text-generation
 library_name: peft
+tags:
+  - llama-2
 ---
-## Training procedure
+
+# CRIA v1.3
+
+💡 [Article](https://walterteng.com/cria) |
+💻 [Github](https://github.com/davzoku/cria) |
+📔 Colab [1](https://colab.research.google.com/drive/1rYTs3qWJerrYwihf1j0f00cnzzcpAfYe),[2](https://colab.research.google.com/drive/1Wjs2I1VHjs6zT_GE42iEXsLtYh6VqiJU)
+
+## What is CRIA?
+
+> krē-ə plural crias. : a baby llama, alpaca, vicuña, or guanaco.
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/davzoku/cria/main/assets/icon-512x512.png" width="300" height="300" alt="Cria Logo"> <br>
+  <i>or what ChatGPT suggests, <b>"Crafting a Rapid prototype of an Intelligent llm App using open source resources"</b>.</i>
+</p>
+
+The initial objective of the CRIA project is to develop a comprehensive end-to-end chatbot system, starting from the instruction-tuning of a large language model and extending to its deployment on the web using frameworks such as Next.js. 
+Specifically, we have fine-tuned the `llama-2-7b-chat-hf` model with QLoRA (4-bit precision) using the [mlabonne/CodeLlama-2-20k](https://huggingface.co/datasets/mlabonne/CodeLlama-2-20k) dataset. This fine-tuned model serves as the backbone for the [CRIA chat](https://chat.walterteng.com) platform.
+
+## 📦 Model Release
+
+CRIA v1.3 comes with several variants.
+
+- [davzoku/cria-llama2-7b-v1.3](https://huggingface.co/davzoku/cria-llama2-7b-v1.3): Merged Model
+- [davzoku/cria-llama2-7b-v1.3-GGML](https://huggingface.co/davzoku/cria-llama2-7b-v1.3-GGML): Quantized Merged Model
+- [davzoku/cria-llama2-7b-v1.3_peft](https://huggingface.co/davzoku/cria-llama2-7b-v1.3_peft): PEFT adapter
+
+## 🔧 Training
+
+It was trained on a Google Colab notebook with a T4 GPU and high RAM.
+
+### Training procedure
 
 
 The following `bitsandbytes` quantization config was used during training:
@@ -14,7 +54,49 @@ The following `bitsandbytes` quantization config was used during training:
 - bnb_4bit_quant_type: nf4
 - bnb_4bit_use_double_quant: False
 - bnb_4bit_compute_dtype: float16
+  
 ### Framework versions
 
 
 - PEFT 0.4.0
+
+
+## 💻 Usage
+
+```python
+# pip install transformers accelerate
+
+from transformers import AutoTokenizer
+import transformers
+import torch
+
+model = "davzoku/cria-llama2-7b-v1.3"
+prompt = "What is a cria?"
+
+tokenizer = AutoTokenizer.from_pretrained(model)
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+
+sequences = pipeline(
+    f'<s>[INST] {prompt} [/INST]',
+    do_sample=True,
+    top_k=10,
+    num_return_sequences=1,
+    eos_token_id=tokenizer.eos_token_id,
+    max_length=200,
+)
+for seq in sequences:
+    print(f"Result: {seq['generated_text']}")
+```
+
+## References
+
+We'd like to thank:
+
+- [mlabonne](https://huggingface.co/mlabonne) for his article and resources on implementation of instruction tuning
+- [TheBloke](https://huggingface.co/TheBloke) for his script for LLM quantization.
+