Update README.md

README.md

@@ -2,213 +2,53 @@
 license: other
 license_name: deepseek-license
 license_link: LICENSE
+pipeline_tag: text-generation
+tags:
+- code
+- mixture-of-experts
+- SarvaCode
+- india-stack
+language:
+- en
+base_model:
+- deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct
 ---
-<!-- markdownlint-disable first-line-h1 -->
-<!-- markdownlint-disable html -->
-<!-- markdownlint-disable no-duplicate-header -->
 
-<div align="center">
-  <img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V2" />
-</div>
-<hr>
-<div align="center" style="line-height: 1;">
-  <a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;">
-    <img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-  <a href="https://chat.deepseek.com/" target="_blank" style="margin: 2px;">
-    <img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20V2-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-  <a href="https://huggingface.co/deepseek-ai" target="_blank" style="margin: 2px;">
-    <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-</div>
+# SarvaCode-16B-Indigenous
 
-<div align="center" style="line-height: 1;">
-  <a href="https://discord.gg/Tc7c45Zzu5" target="_blank" style="margin: 2px;">
-    <img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-  <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg?raw=true" target="_blank" style="margin: 2px;">
-    <img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-  <a href="https://twitter.com/deepseek_ai" target="_blank" style="margin: 2px;">
-    <img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-</div>
+**SarvaCode** is an indigenously customized, open-source Mixture-of-Experts (MoE) code language model. It is built upon the DeepSeek-Coder-V2 architecture but optimized for the **Indian Software Ecosystem**. 
 
-<div align="center" style="line-height: 1;">
-  <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/LICENSE-CODE" style="margin: 2px;">
-    <img alt="Code License" src="https://img.shields.io/badge/Code_License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-  <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/LICENSE-MODEL" style="margin: 2px;">
-    <img alt="Model License" src="https://img.shields.io/badge/Model_License-Model_Agreement-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-</div>
-<p align="center">
-  <a href="#4-api-platform">API Platform</a> |
-  <a href="#5-how-to-run-locally">How to Use</a> |
-  <a href="#6-license">License</a> |
-</p>
+While global models focus on general code, SarvaCode is fine-tuned to understand **Indian English instructions**, local financial protocols (GST, TDS), and the technical frameworks of **India Stack** (UPI, ONDC, Aadhaar/UIDAI).
 
+## 1. Key Improvements
+Compared to the base Lite model, **SarvaCode** features:
+- **Higher Active Parameters:** Increased from 6 to **8 active experts per token**, boosting reasoning power to **~3.2B active parameters** per message.
+- **Indigenous Logic:** Enhanced accuracy for Indian-specific tasks like GST calculation logic, IFSC validation, and regional date/currency formatting.
+- **India Stack Awareness:** Pre-loaded context for integrating with NPCI (UPI), ONDC, and DigiLocker APIs.
+- **Massive Context:** Maintains a **128K context window** to digest entire Indian government technical gazettes or large codebases in one go.
 
-<p align="center">
-  <a href="https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/paper.pdf"><b>Paper Link</b>👁️</a>
-</p>
+## 2. Model Specifications
 
-# DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence
+| **Model** | **#Total Params** | **#Active Params** | **Context Length** | **Specialization** |
+| :---: | :---: | :---: | :---: | :---: |
+| **SarvaCode-16B** | 16B | **3.2B** | 128k | India Stack & Fintech |
 
-## 1. Introduction
-We present DeepSeek-Coder-V2, an open-source Mixture-of-Experts (MoE) code language model that achieves performance comparable to GPT4-Turbo in code-specific tasks. Specifically, DeepSeek-Coder-V2 is further pre-trained from an intermediate checkpoint of DeepSeek-V2 with additional 6 trillion tokens. Through this continued pre-training, DeepSeek-Coder-V2 substantially enhances the coding and mathematical reasoning capabilities of DeepSeek-V2, while maintaining comparable performance in general language tasks. Compared to DeepSeek-Coder-33B, DeepSeek-Coder-V2 demonstrates significant advancements in various aspects of code-related tasks, as well as reasoning and general capabilities. Additionally, DeepSeek-Coder-V2 expands its support for programming languages from 86 to 338, while extending the context length from 16K to 128K. 
+## 3. How to Run Locally
 
-<p align="center">
-  <img width="100%" src="https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/figures/performance.png?raw=true">
-</p>
-
-
-In standard benchmark evaluations, DeepSeek-Coder-V2 achieves superior performance compared to closed-source models such as GPT4-Turbo, Claude 3 Opus, and Gemini 1.5 Pro in coding and math benchmarks.  The list of supported programming languages can be found [here](https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/supported_langs.txt).
-
-## 2. Model Downloads
-
-We release the DeepSeek-Coder-V2 with 16B and 236B parameters based on the [DeepSeekMoE](https://arxiv.org/pdf/2401.06066) framework, which has actived parameters of only 2.4B and 21B , including base and instruct models, to the public. 
-
-<div align="center">
-
-|            **Model**            | **#Total Params** | **#Active Params** | **Context Length** |                         **Download**                         |
-| :-----------------------------: | :---------------: | :----------------: | :----------------: | :----------------------------------------------------------: |
-|   DeepSeek-Coder-V2-Lite-Base   |        16B        |        2.4B        |        128k        | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Base) |
-| DeepSeek-Coder-V2-Lite-Instruct |        16B        |        2.4B        |        128k        | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct) |
-|     DeepSeek-Coder-V2-Base      |       236B        |        21B         |        128k        | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Base) |
-|   DeepSeek-Coder-V2-Instruct    |       236B        |        21B         |        128k        | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Instruct) |
-
-</div>
-
-
-## 3. Chat Website
-
-You can chat with the DeepSeek-Coder-V2 on DeepSeek's official website: [coder.deepseek.com](https://coder.deepseek.com/sign_in)
-
-## 4. API Platform
-We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.com](https://platform.deepseek.com/), and you can also pay-as-you-go at an unbeatable price.
-<p align="center">
-  <img width="40%" src="https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/figures/model_price.jpg?raw=true">
-</p>
-
-
-## 5. How to run locally
-**Here, we provide some examples of how to use DeepSeek-Coder-V2-Lite model. If you want to utilize DeepSeek-Coder-V2 in BF16 format for inference, 80GB*8 GPUs are required.**
-
-### Inference with Huggingface's Transformers
-You can directly employ [Huggingface's Transformers](https://github.com/huggingface/transformers) for model inference.
-
-#### Code Completion
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
-tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
-input_text = "#write a quick sort algorithm"
-inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
-outputs = model.generate(**inputs, max_length=128)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
-```
-
-#### Code Insertion
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
-tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
-input_text = """<｜fim▁begin｜>def quick_sort(arr):
-    if len(arr) <= 1:
-        return arr
-    pivot = arr[0]
-    left = []
-    right = []
-<｜fim▁hole｜>
-        if arr[i] < pivot:
-            left.append(arr[i])
-        else:
-            right.append(arr[i])
-    return quick_sort(left) + [pivot] + quick_sort(right)<｜fim▁end｜>"""
-inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
-outputs = model.generate(**inputs, max_length=128)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True)[len(input_text):])
-```
-
-#### Chat Completion
+### Inference with Transformers
+Ensure you use `trust_remote_code=True` to load the specialized MoE configuration.
 
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
-tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct", trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
-messages=[
-    { 'role': 'user', 'content': "write a quick sort algorithm in python."}
-]
-inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
-# tokenizer.eos_token_id is the id of <｜end▁of▁sentence｜>  token
-outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)
-print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
-```
 
+model_path = "./SarvaCode" # Your local directory
+tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
 
+# Example: Indian Financial Logic
+input_text = "User: Write a Python function to calculate the GST for a service with an 18% slab, ensuring the output separates CGST and SGST.\n\nAssistant:"
 
-The complete chat template can be found within `tokenizer_config.json` located in the huggingface model repository.
-
-An example of chat template is as belows:
-
-```bash
-<｜begin▁of▁sentence｜>User: {user_message_1}
-
-Assistant: {assistant_message_1}<｜end▁of▁sentence｜>User: {user_message_2}
-
-Assistant:
-```
-
-You can also add an optional system message:
-
-```bash
-<｜begin▁of▁sentence｜>{system_message}
-
-User: {user_message_1}
-
-Assistant: {assistant_message_1}<｜end▁of▁sentence｜>User: {user_message_2}
-
-Assistant:
-```
-
-### Inference with vLLM (recommended)
-To utilize [vLLM](https://github.com/vllm-project/vllm) for model inference, please merge this Pull Request into your vLLM codebase: https://github.com/vllm-project/vllm/pull/4650.
-
-```python
-from transformers import AutoTokenizer
-from vllm import LLM, SamplingParams
-
-max_model_len, tp_size = 8192, 1
-model_name = "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-llm = LLM(model=model_name, tensor_parallel_size=tp_size, max_model_len=max_model_len, trust_remote_code=True, enforce_eager=True)
-sampling_params = SamplingParams(temperature=0.3, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id])
-
-messages_list = [
-    [{"role": "user", "content": "Who are you?"}],
-    [{"role": "user", "content": "write a quick sort algorithm in python."}],
-    [{"role": "user", "content": "Write a piece of quicksort code in C++."}],
-]
-
-prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list]
-
-outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params)
-
-generated_text = [output.outputs[0].text for output in outputs]
-print(generated_text)
-```
-
-
-
-## 6. License
-
-This code repository is licensed under [the MIT License](https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/LICENSE-CODE). The use of DeepSeek-Coder-V2 Base/Instruct models is subject to [the Model License](https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/LICENSE-MODEL). DeepSeek-Coder-V2 series (including Base and Instruct) supports commercial use.
-
-
-## 7. Contact
-If you have any questions, please raise an issue or contact us at [service@deepseek.com](service@deepseek.com).
+inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=256)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))