Update README.md

README.md

@@ -8,56 +8,44 @@ tags:
 base_model:
 - M4-ai/tau-0.5B
 - Qwen/Qwen1.5-0.5B
+license: cc-by-sa-4.0
+datasets:
+- Locutusque/UltraTextbooks-2.0
 ---
 
 # tau-0.5B
 
-tau-0.5B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
-* [M4-ai/tau-0.5B](https://huggingface.co/M4-ai/tau-0.5B)
-* [Qwen/Qwen1.5-0.5B](https://huggingface.co/Qwen/Qwen1.5-0.5B)
-
-## 🧩 Configuration
-
-```yaml
-slices:
-  - sources:
-      - model: M4-ai/tau-0.5B
-        layer_range: [0, 24]
-      - model: Qwen/Qwen1.5-0.5B
-        layer_range: [0, 24]
-merge_method: slerp
-base_model: M4-ai/tau-0.5B
-parameters:
-  t:
-    - filter: self_attn
-      value: [0, 0.5, 0.3, 0.7, 1]
-    - filter: mlp
-      value: [1, 0.5, 0.7, 0.3, 0]
-    - value: 0.5
-dtype: bfloat16
-```
-
-## 💻 Usage
-
-```python
-!pip install -qU transformers accelerate
-
-from transformers import AutoTokenizer
-import transformers
-import torch
-
-model = "M4-ai/tau-0.5B"
-messages = [{"role": "user", "content": "What is a large language model?"}]
-
-tokenizer = AutoTokenizer.from_pretrained(model)
-prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-pipeline = transformers.pipeline(
-    "text-generation",
-    model=model,
-    torch_dtype=torch.float16,
-    device_map="auto",
-)
-
-outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
-print(outputs[0]["generated_text"])
-```
+## Model Details
+- **Model Name:** tau-0.5B
+- **Base Model:** Qwen1.5-0.5B
+- **Dataset:** UltraTextbooks-2.0
+- **Model Size:** 0.5B parameters
+- **Model Type:** Language Model
+- **Training Procedure:** Further pre-training of Qwen1.5-0.5B on UltraTextbooks-2.0, followed by merging back to the base model using SLERP to prevent catastrophic forgetting.
+
+## Model Use
+tau-0.5B is designed to be a general-purpose language model with enhanced capabilities in the domains of machine learning, mathematics, and coding. It can be used for a wide range of natural language processing tasks, such as:
+- Educational question answering
+- Text summarization
+- Content generation for educational purposes
+- Code understanding and generation
+- Mathematical problem solving
+
+The model's exposure to the diverse content in the UltraTextbooks-2.0 dataset makes it particularly well-suited for applications in educational technology and research.
+
+## Training Data
+tau-0.5B was further pre-trained on the UltraTextbooks-2.0 dataset, which is an expanded version of the original UltraTextbooks dataset. UltraTextbooks-2.0 incorporates additional high-quality synthetic and human-written textbooks from various sources on the Hugging Face platform, with a focus on increasing the diversity of content in the domains of machine learning, mathematics, and coding.
+
+For more details on the dataset, please refer to the [UltraTextbooks-2.0 Dataset Card](https://example.com/ultratextbooks-2.0-dataset-card).
+
+## Performance and Limitations
+Evaluations are yet to be performed. It is essential to note that the model may still exhibit biases or inaccuracies present in the training data. Users are encouraged to critically evaluate the model's outputs and report any issues to facilitate continuous improvement.
+
+## Environmental Impact
+The training of tau-0.5B required computational resources that contribute to the model's overall environmental impact. However, efforts were made to optimize the training process and minimize the carbon footprint.
+
+## Ethical Considerations
+tau-0.5B was trained on a diverse dataset that may contain biases and inaccuracies. Users should be aware of these potential limitations and use the model responsibly. The model should not be used for tasks that could cause harm or discriminate against individuals or groups.
+
+## Usage Rights
+tau-0.5B is released under the cc-by-sa-4.0 license, allowing for both non-commercial and commercial use. Users are required to attribute the model to its creators and share any derivative works under the same license.