Update README.md

README.md

@@ -1,57 +1,86 @@
 ---
-license: other
-base_model: microsoft/phi-1_5
+library_name: transformers
 tags:
-- llama-factory
-- generated_from_trainer
-model-index:
-- name: path_to_sft_checkpoint
-  results: []
+- code
+
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
 
-# path_to_sft_checkpoint
+# Bud Code Millenials 1B
+
+Welcome to our Code Model repository! Our model is specifically fine-tuned for code generation tasks. Bud Millenial Code Gen open-source models are currently the State of the Art (SOTA) for code generation, beating all the existing models of all sizes. We have achieved a HumanEval value of 80.48 @ Pass 1, beating proprietary models like Gemini Ultra, Claude, GPT-3.5 etc. by a large margin, and on par with GPT-4 (HumanEval ~ 82. Ref. WizardCoder). Our proprietary model (Bud Code Jr) beats GPT-4 as well with a HumanEval value of 88.2 & a context size of 168K, we will be releasing an API for Researchers, Enterprises, and potential Partners by January 2024 end. If interested, please reach out to [email protected]
+
+### 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="https://raw.githubusercontent.com/BudEcosystem/code-millenials/main/assets/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 (+) | MBPP (+) |
+|---------|-------------|---------------|----------|
+|Code Millenials 34B | <a href="https://huggingface.co/budecosystem/code-millenials-34b" target="_blank">HF Link</a> | 80.48 (75) | 74.68 (62.9) |
+|Code Millenials 13B | <a href="https://huggingface.co/budecosystem/code-millenials-13b" target="_blank">HF Link</a> | 76.21 (69.5) | 70.17 (57.6) |
+|Code Millenials 3B | <a href="https://huggingface.co/budecosystem/code-millenials-3b" target="_blank">HF Link</a> | - | - |
+|Code Millenials 1B | <a href="https://huggingface.co/budecosystem/code-millenials-1b" target="_blank">HF Link</a> | - | - |
+
+
+
 
-This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the oss-evol dataset.
+### 🚀 Quick Start
 
-## Model description
+Inference code  using the pre-trained model from the Hugging Face model hub
 
-More information needed
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-## Intended uses & limitations
+tokenizer = AutoTokenizer.from_pretrained("budecosystem/code-millenials-1b")
+model = AutoModelForCausalLM.from_pretrained("budecosystem/code-millenials-1b")
 
-More information needed
+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:"""
 
-## Training and evaluation data
+instruction = <Your code instruction here>
 
-More information needed
+prompt = template.format(instruction=instruction)
 
-## Training procedure
+inputs = tokenizer(prompt, return_tensors="pt")
+sample = model.generate(**inputs, max_length=128)
+print(tokenizer.decode(sample[0]))
 
-### Training hyperparameters
+```
 
-The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 6
-- eval_batch_size: 8
-- seed: 42
-- distributed_type: multi-GPU
-- num_devices: 8
-- total_train_batch_size: 48
-- total_eval_batch_size: 64
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: cosine
-- num_epochs: 3.0
 
-### Training results
+## Training details
 
+The model is trained of 8 A100 80GB for approximately 6hrs. 
 
+| Hyperparameters              | Value  |
+| :----------------------------| :-----: |
+| per_device_train_batch_size  | 6      |
+| gradient_accumulation_steps  | 1      |
+| epoch | 3 |
+| steps | 11502 |
+| learning_rate                | 2e-5   |
+| lr schedular type | cosine |
+| warmup ratio | 0.1 |
+| optimizer                    | adamw  |
+| fp16                         | True   |
+| GPU                          | 8 A100 80GB |
 
-### Framework versions
+### Important Note
 
-- Transformers 4.34.1
-- Pytorch 2.1.1+cu121
-- Datasets 2.14.7
-- Tokenizers 0.14.1
+- **Bias, Risks, and Limitations:** Model may sometimes make errors, produce misleading contents, or struggle to manage tasks that are not related to coding.