Continue-1-OSS

Advanced Text Generation Model

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:

pip install transformers torch
pip install vllm  # For fast inference (optional but recommended)

Quickstart

Basic Usage

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:

pip install vllm

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

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:

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

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"
)

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Developed by SVECTOR