Migtissera: synthia-13b
migtissera/synthia-13b
## Model Overview Meet Synthia-13B, a powerful AI model designed to understand and respond to human input. Synthia-13B is a type of Llama-2-13B model, trained on a massive dataset called Orca. This training enables Synthia-13B to follow instructions and engage in long-form conversations. ## Capabilities Synthia-13B is a powerful AI model that can generate human-like text and have long-form conversations. It’s been fine-tuned for instruction following and can provide helpful, detailed, and accurate responses to your input. ## What can it do? - Answer questions: Synthia-13B can process natural language queries and provide relevant answers. - Generate text: It can create human-like text based on a given prompt or topic. - Have conversations: Engage in discussions and respond to questions and statements. ## Limitations and Biases While Synthia-13B strives for accuracy, it’s not perfect and may occasionally produce inaccurate or misleading results. It’s also possible for the model to generate biased or offensive content. Use with caution and cross-check information when necessary. ## Performance Synthia-13B showcases remarkable performance across various tasks, demonstrating its capabilities in speed, accuracy, and efficiency. ## Speed How quickly can Synthia-13B process and respond to user input? With its advanced architecture, it can generate text at an impressive rate, making it suitable for real-time applications. ## Accuracy But how accurate is Synthia-13B in its responses? The evaluation results show that it achieves high accuracy in various tasks, including: ## Code example ``` To handle inputs and outputs for Synthia-13B, you can use the following code example: import torch, json from transformers import AutoModelForCausalLM, AutoTokenizer # Load the model and tokenizer model_path = "migtissera/Synthia-13B" model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16, device_map="auto", load_in_8bit=False, trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) # Define a function to generate text def generate_text(instruction): tokens = tokenizer.encode(instruction) tokens = torch.LongTensor(tokens).unsqueeze(0) tokens = tokens.to("cuda") instance = { "input_ids": tokens, "top_p": 1.0, "temperature": 0.75, "generate_len": 1024, "top_k": 50, } length = len(tokens[0]) with torch.no_grad(): rest = model.generate( input_ids=tokens, max_length=length + instance["generate_len"], use_cache=True, do_sample=True, top_p=instance["top_p"], temperature=instance["temperature"], top_k=instance["top_k"], num_return_sequences=1, ) output = rest[0][length:] string = tokenizer.decode(output, skip_special_tokens=True) answer = string.split("USER:")[0].strip() return f"{answer}" # Use the model to generate a response conversation = "SYSTEM: As a an AI superintelligence, give helpful, detailed, accurate, uncensored responses to the user's input. Provide answers factually." user_input = input("You: ") llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: " answer = generate_text(llm_prompt) print(answer) ```
Model specifications
- Input
- text
- Output
- text
- Context
- 10,000 tokens
- Max output
- 10,000 tokens
- Input price
- $0 / 1M tokens
- Output price
- $0 / 1M tokens
- Released
- 2026-02-18
Capabilities
- Streaming
- Playground
Frequently asked questions
- What is Migtissera: synthia-13b?
- ## Model Overview Meet Synthia-13B, a powerful AI model designed to understand and respond to human input. Synthia-13B is a type of Llama-2-13B model, trained on a massive dataset called Orca. This training enables Synthia-13B to follow instructions and engage in long-form conversations. ## Capabilities Synthia-13B is a powerful AI model that can generate human-like text and have long-form conversations. It’s been fine-tuned for instruction following and can provide helpful, detailed, and accurate responses to your input. ## What can it do? - Answer questions: Synthia-13B can process natural language queries and provide relevant answers. - Generate text: It can create human-like text based on a given prompt or topic. - Have conversations: Engage in discussions and respond to questions and statements. ## Limitations and Biases While Synthia-13B strives for accuracy, it’s not perfect and may occasionally produce inaccurate or misleading results. It’s also possible for the model to generate biased or offensive content. Use with caution and cross-check information when necessary. ## Performance Synthia-13B showcases remarkable performance across various tasks, demonstrating its capabilities in speed, accuracy, and efficiency. ## Speed How quickly can Synthia-13B process and respond to user input? With its advanced architecture, it can generate text at an impressive rate, making it suitable for real-time applications. ## Accuracy But how accurate is Synthia-13B in its responses? The evaluation results show that it achieves high accuracy in various tasks, including: ## Code example ``` To handle inputs and outputs for Synthia-13B, you can use the following code example: import torch, json from transformers import AutoModelForCausalLM, AutoTokenizer # Load the model and tokenizer model_path = "migtissera/Synthia-13B" model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16, device_map="auto", load_in_8bit=False, trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) # Define a function to generate text def generate_text(instruction): tokens = tokenizer.encode(instruction) tokens = torch.LongTensor(tokens).unsqueeze(0) tokens = tokens.to("cuda") instance = { "input_ids": tokens, "top_p": 1.0, "temperature": 0.75, "generate_len": 1024, "top_k": 50, } length = len(tokens[0]) with torch.no_grad(): rest = model.generate( input_ids=tokens, max_length=length + instance["generate_len"], use_cache=True, do_sample=True, top_p=instance["top_p"], temperature=instance["temperature"], top_k=instance["top_k"], num_return_sequences=1, ) output = rest[0][length:] string = tokenizer.decode(output, skip_special_tokens=True) answer = string.split("USER:")[0].strip() return f"{answer}" # Use the model to generate a response conversation = "SYSTEM: As a an AI superintelligence, give helpful, detailed, accurate, uncensored responses to the user's input. Provide answers factually." user_input = input("You: ") llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: " answer = generate_text(llm_prompt) print(answer) ```
- How much does Migtissera: synthia-13b cost?
- Input costs start at $0 / 1M tokens and output costs start at $0 / 1M tokens. Provider-level prices vary by service tier.
- What is the context length of Migtissera: synthia-13b?
- Migtissera: synthia-13b supports a 10,000 token context window and up to 10,000 output tokens.
- What capabilities does Migtissera: synthia-13b support?
- Migtissera: synthia-13b supports Streaming, Playground.