Update README.md

README.md

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 ---
 license: llama2
+metrics:
+- code_eval
+library_name: transformers
+tags:
+- code
 ---
+
+
+# Introducing Code Millenials 34B
+
+Welcome to our Code Model repository! Our model is specifically fine-tuned for code generation tasks, aiming to revolutionize how systems understand and translate natural language instructions into code queries. Built on CodeLLaMa Python 34B, our model has been meticulously fine-tuned with a curated code generation instructions, ensuring quality and precision. 
+
+### News 🔥🔥🔥
+
+- [2024/01/03] We released **Code Millenials 34B** , which achieves the **80.48 pass@1** on the [HumanEval Benchmarks](https://github.com/openai/human-eval).
+- [2024/01/02] We released **Code Millenials 13B** , which achieves the **76.21 pass@1** on the [HumanEval Benchmarks](https://github.com/openai/human-eval).
+
+
+### HumanEval
+
+<p align="center" width="100%">
+<a ><img src="result.png" alt="CodeMillenials" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a>
+</p>
+
+For the millenial models, the eval script in the github repo is used for the above result.
+
+Note: The humaneval values of other models are taken from the official repos of [WizardCoder](https://github.com/nlpxucan/WizardLM), [DeepseekCoder](https://github.com/deepseek-ai/deepseek-coder), [Gemini](https://deepmind.google/technologies/gemini/#capabilities) etc. 
+
+
+### Models
+
+|   Model | Checkpoint  | HumanEval |
+|---------|-------------|-----------|
+|Code Millenials 34B | <a href="https://huggingface.co/budecosystem/code-millenials-34b" target="_blank">HF Link</a> | 80.48 |
+|Code Millenials 13B | <a href="https://huggingface.co/budecosystem/code-millenials-13b" target="_blank">HF Link</a> | 76.21 |
+
+
+
+
+### 🚀 Quick Start
+
+Inference code  using the pre-trained model from the Hugging Face model hub
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("budecosystem/code-millenials-34b")
+model = AutoModelForCausalLM.from_pretrained("budecosystem/code-millenials-34b")
+
+template = """A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
+### Instruction: {instruction} ### Response:"""
+
+instruction = <Your code instruction here>
+
+prompt = template.format(instruction=instruction)
+
+inputs = tokenizer(prompt, return_tensors="pt")
+sample = model.generate(**inputs, max_length=128)
+print(tokenizer.decode(sample[0]))
+
+```
+
+
+## Training details
+
+The model is trained of 16 A100 80GB for approximately 50hrs. 
+
+| Hyperparameters              | Value  |
+| :----------------------------| :-----: |
+| per_device_train_batch_size  | 16      |
+| gradient_accumulation_steps  | 1      |
+| epoch | 3 |
+| steps | 2157 |
+| learning_rate                | 2e-5   |
+| lr schedular type | cosine |
+| warmup ratio | 0.1 |
+| optimizer                    | adamw  |
+| fp16                         | True   |
+| GPU                          | 16 A100 80GB |
+
+### Important Note
+
+- **Bias, Risks, and Limitations:** Model may sometimes make errors, produce misleading contents, or struggle to manage tasks that are not related to coding.