metadata
license: apache-2.0
tags:
- text-generation
- text
- chat
pipeline_tag: text-generation
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
Temperature Settings:
- 0.0-0.3 for factual/deterministic tasks
- 0.7-0.9 for creative tasks
Context Management:
- Model supports 128K tokens but consider truncating for faster inference
- Use sliding window for very long documents
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"
)
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 file for complete details.
Developed by SVECTOR