Update README.md

README.md

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----
-license: mit
----
+---
+license: apache-2.0
+tags:
+- text-generation
+pipeline_tag: text-generation
+---
+
+<p align="center">
+  <img alt="Continue-1-OSS" src="https://github.com/SVECTOR-CORPORATION/Continue-1-OSS/blob/main/Continue-1-OSS-image-banner.jpg?raw=true" width="800">
+</p>
+
+# Continue-1-OSS
+
+### Advanced Text Generation Model
+
+<div align="left" style="line-height: 1;">
+  <a href="https://spec-chat.tech" target="_blank" style="margin: 2px;">
+    <img alt="SVECTOR" src="https://img.shields.io/badge/💬%20Spec%20Chat-Spec%20Chat-blue?style=plastic" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  
+  <a href="https://huggingface.co/SVECTOR-CORPORATION" target="_blank" style="margin: 2px;">
+    <img alt="SVECTOR" src="https://img.shields.io/badge/🤗%20Hugging%20Face-SVECTOR-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  
+  <a href="https://huggingface.co/SVECTOR-CORPORATION/Continue-1-OSS/blob/main/LICENSE" style="margin: 2px;">
+    <img alt="License" src="https://img.shields.io/badge/License-Apache%202.0-blue?color=1e88e5&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  
+  <a href="https://github.com/SVECTOR-CORPORATION/Continue-1-OSS" target="_blank" style="margin: 2px;">
+    <img alt="GitHub" src="https://img.shields.io/badge/GitHub-Continue--1--OSS-181717?logo=github&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+</div>
+
+## Introduction
+
+We are thrilled to introduce **Continue-1-OSS**, an advanced text generation model developed by SVECTOR, built on the Continue-1 architecture optimized for high-quality text generation, instruction following, and long-context understanding.
+
+**Continue-1-OSS** is engineered to provide:
+
+- **Superior Instruction Following:** Accurately follows complex, multi-step instructions
+- **Long Context:** Robust handling of up to 128K+ tokens
+- **Natural Conversations:** Human-like dialogue with strong reasoning capabilities
+- **Tool Integration:** Built-in support for function calling and external tool use
+- **Open Source:** Fully accessible under Apache 2.0 license for research and commercial use
+
+This model combines the power of transformer architecture with advanced training techniques to deliver exceptional performance across a wide range of natural language tasks.
+
+### Model Specifications
+
+- **Base Architecture:** Continue1ForCausalLM (transformer decoder)
+- **Model Type:** continue_oss
+- **Parameters:** 3 Billion
+- **Context Length:** 131,072 tokens
+- **Vocabulary Size:** 128,256 tokens
+- **Hidden Size:** 3072
+- **Number of Layers:** 28
+- **Attention Heads:** 24
+- **License:** Apache 2.0
+
+
+## Requirements
+
+To use Continue-1-OSS, install the required dependencies:
+
+```bash
+pip install transformers torch
+pip install vllm  # For fast inference (optional but recommended)
+```
+
+## Quickstart
+
+### Basic Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "SVECTOR-CORPORATION/Continue-1-OSS"
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    trust_remote_code=True,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+# Prepare conversation
+messages = [
+    {"role": "user", "content": "What is machine learning?"}
+]
+
+# Apply chat template and generate
+input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
+inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=512,
+    temperature=0.7,
+    top_p=0.9,
+    do_sample=True
+)
+
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+### Using vLLM (Recommended for Production)
+
+For high-performance inference with faster generation:
+
+```bash
+pip install vllm
+```
+
+```python
+from vllm import LLM, SamplingParams
+
+# Initialize model
+llm = LLM(
+    model="SVECTOR-CORPORATION/Continue-1-OSS",
+    trust_remote_code=True,
+    max_model_len=8192
+)
+
+# Set sampling parameters
+sampling_params = SamplingParams(
+    temperature=0.7,
+    top_p=0.9,
+    max_tokens=512
+)
+
+# Generate
+messages = [
+    {"role": "user", "content": "Explain quantum computing in simple terms."}
+]
+
+outputs = llm.chat(messages, sampling_params=sampling_params)
+print(outputs[0].outputs[0].text)
+```
+
+**Default System Prompt:** "You are Continue-1-OSS, an advanced AI assistant developed by SVECTOR. You are designed to be helpful, harmless, and honest."
+
+## Advanced Features
+
+### Multi-Turn Conversations
+
+```python
+messages = [
+    {"role": "system", "content": "You are Continue-1-OSS, a helpful AI assistant."},
+    {"role": "user", "content": "What is quantum computing?"},
+    {"role": "assistant", "content": "Quantum computing is a type of computing that uses quantum mechanics principles..."},
+    {"role": "user", "content": "Can you explain that more simply?"}
+]
+```
+
+### Tool Calling Support
+
+Continue-1-OSS supports function calling for tool integration:
+
+```python
+messages = [
+    {"role": "user", "content": "What's the weather in San Francisco?"}
+]
+
+# Model can generate JSON function calls
+# Example output: {"name": "get_weather", "parameters": {"location": "Ahmedabad"}}
+```
+
+
+## Use Cases
+
+Continue-1-OSS excels at:
+
+- **Conversational AI:** Build chatbots and virtual assistants with natural dialogue
+- **Content Generation:** Generate articles, stories, and creative content
+- **Code Assistance:** Help with coding tasks, debugging, and code explanations
+- **Question Answering:** Answer questions based on context with high accuracy
+- **Summarization:** Condense long documents into concise summaries
+- **Data Extraction:** Extract structured data from unstructured text
+- **Tool Integration:** Call functions and use external tools intelligently
+- **Education:** Create educational content and tutoring assistance
+- **Customer Service:** Automated support with natural language understanding
+
+## Performance
+
+- **Quality:** State-of-the-art instruction following and text generation
+- **Speed:** Fast inference with vLLM optimization
+- **Memory:** ~7GB GPU RAM (BF16), ~14GB (FP32)
+- **Context:** Handles up to 128K tokens effectively
+- **Efficiency:** Competitive with much larger models on many tasks
+
+## Model Architecture
+
+Continue-1-OSS uses a custom architecture based on the transformer decoder:
+
+- **Architecture Class:** `Continue1ForCausalLM`
+- **Config Class:** `Continue1Config`
+- **Hidden Size:** 3072
+- **Num Layers:** 28
+- **Num Attention Heads:** 24
+- **Intermediate Size:** 8192
+- **Vocab Size:** 128,256
+- **Max Position Embeddings:** 131,072
+
+The model uses RoPE (Rotary Position Embeddings) for positional encoding and supports extended context through position interpolation.
+
+## Training
+
+Continue-1-OSS was developed using:
+- High-quality instruction datasets covering diverse tasks
+- Conversational and reasoning data for improved dialogue
+- Code and technical content for developer assistance
+- Multi-turn dialogue for contextual understanding
+
+Training utilized:
+- Advanced optimization techniques
+- Careful hyperparameter tuning
+- Quality filtering and data curation
+- Evaluation on diverse benchmarks
+
+
+## Limitations
+
+As with any language model, Continue-1-OSS has certain limitations:
+
+- **Knowledge Cutoff:** Training data is limited to information available up to December 2023
+- **Factual Accuracy:** May occasionally generate incorrect or outdated information
+- **Specialized Domains:** Performance may vary on highly specialized technical knowledge
+- **Long Context:** Very long contexts (>64K tokens) may impact generation quality
+- **Languages:** Primarily optimized for English; other languages have limited support
+- **Reasoning:** Complex multi-step reasoning may require careful prompting
+- **Compute:** Requires GPU for optimal performance (CPU is significantly slower)
+
+## Ethical Considerations
+
+SVECTOR is committed to responsible AI development. Users should:
+
+- **Transparency:** Disclose when content is AI-generated
+- **Verification:** Always fact-check important information generated by the model
+- **Bias Awareness:** Be aware the model may reflect biases present in training data
+- **Privacy:** Do not input personal or sensitive information without proper safeguards
+- **Safety:** Implement content filtering and guardrails for production applications
+- **Responsible Use:** Do not use for illegal purposes, misinformation, or harmful content
+- **Attribution:** Credit the model when used in public projects or research
+
+## Performance Tips
+
+1. **Temperature Settings:**
+   - 0.0-0.3 for factual/deterministic tasks
+   - 0.7-0.9 for creative tasks
+   
+2. **Context Management:**
+   - Model supports 128K tokens but consider truncating for faster inference
+   - Use sliding window for very long documents
+
+3. **Batch Processing:**
+   - Use vLLM for efficient batched inference in production
+   - Group similar-length prompts together
+
+```python
+from transformers import AutoModelForCausalLM, BitsAndBytesConfig
+import torch
+
+quantization_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype=torch.bfloat16
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    "SVECTOR-CORPORATION/Continue-1-OSS",
+    trust_remote_code=True,
+    quantization_config=quantization_config,
+    device_map="auto"
+)
+```
+
+
+## License
+
+This model is released under the **Apache License 2.0**. You are free to use, modify, and distribute this model for both commercial and non-commercial purposes. See the [LICENSE](https://huggingface.co/SVECTOR-CORPORATION/Continue-1-OSS/blob/main/LICENSE) file for complete details.
+
+---
+
+<p align="center">
+    <i>Developed by <a href="https://www.svector.co.in">SVECTOR</a></i>
+</p>