Update README.md

README.md

@@ -1,60 +1,106 @@
 ---
-license: other
-base_model: meta-llama/Meta-Llama-3-8B
+license: llama2
+library_name: transformers
 tags:
-- llama-factory
-- full
-- generated_from_trainer
+- code
 model-index:
-- name: Meta-Llama-3-8B-pretrain-oss-evol
-  results: []
+- name: Code Millenials
+  results:
+  - task:
+      type: text-generation
+    dataset:
+      name: HumanEval
+      type: openai_humaneval
+    metrics:
+    - type: pass@1
+      value: 0.671
+      name: pass@1
+      verified: false
 ---
 
-<!-- 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. -->
 
-# Meta-Llama-3-8B-pretrain-oss-evol
+# Bud Code Millenials 8B
 
-This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the oss-evol dataset.
+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 jithinvg@bud.studio
+### News 🔥🔥🔥
 
-## Model description
+- [2024/04/21] We released **Code Millenials 8B** , which achieves the **67.1 pass@1** on the [HumanEval Benchmarks](https://github.com/openai/human-eval).
+- [2024/01/09] We released **Code Millenials 3B** , which achieves the **56.09 pass@1** on the [HumanEval Benchmarks](https://github.com/openai/human-eval).
+- [2024/01/09] We released **Code Millenials 1B** , which achieves the **51.82 pass@1** on the [HumanEval Benchmarks](https://github.com/openai/human-eval).
+- [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).
 
-More information needed
 
-## Intended uses & limitations
+### HumanEval
 
-More information needed
+<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>
 
-## Training and evaluation data
+For the millenial models, the eval script in the github repo is used for the above result.
 
-More information needed
+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. 
 
-## Training procedure
 
-### Training hyperparameters
+### Models
 
-The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 8
-- eval_batch_size: 8
-- seed: 42
-- distributed_type: multi-GPU
-- num_devices: 8
-- total_train_batch_size: 64
-- total_eval_batch_size: 64
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: cosine
-- lr_scheduler_warmup_ratio: 0.1
-- num_epochs: 3.0
-- mixed_precision_training: Native AMP
+|   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-8b" target="_blank">HF Link</a> | 67.1 (61.6) | - |
+|Code Millenials 3B | <a href="https://huggingface.co/budecosystem/code-millenials-3b" target="_blank">HF Link</a> | 56.09 (52.43) | 55.13 (47.11) |
+|Code Millenials 1B | <a href="https://huggingface.co/budecosystem/code-millenials-1b" target="_blank">HF Link</a> | 51.82 (48.17) | 53.13 (44.61) |
 
-### Training results
 
 
 
-### Framework versions
+### 🚀 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-8b")
+model = AutoModelForCausalLM.from_pretrained("budecosystem/code-millenials-8b")
+
+template = """You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.
+
+### 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.
 
-- Transformers 4.40.0
-- Pytorch 2.2.2+cu121
-- Datasets 2.19.0
-- Tokenizers 0.19.1