Sombrero Opus 14B Sm5

🚀 Sombrero-Opus-14B-Sm5

Sombrero-Opus-14B-Sm5 is based on the Qwen 2.5 14B modality architecture, aiming to enhance coding efficiency and computational reasoning. It can be used for various tasks such as code generation, problem - solving, and technical documentation.

🚀 Quick Start

Here is a code snippet with apply_chat_template to show you how to load the tokenizer and model and generate content:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Sombrero-Opus-14B-Sm5"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Write a Python function to find the Fibonacci sequence."
messages = [
    {"role": "system", "content": "You are an advanced coding assistant."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

✨ Features

Key Improvements

1. Optimized for Coding: The model specializes in generating high - quality, structured code with minimal redundant tokens, ensuring efficient execution.
2. Enhanced Memory Utilization: Implements streamlined memory optimization to reduce computational overhead and improve performance.
3. Superior Reasoning Capabilities: Excels in solving complex mathematical and algorithmic problems with logical and structured explanations.
4. Long - Context Support: Supports up to 128K tokens for input context and can generate up to 8K tokens in a single output, making it ideal for detailed coding responses.
5. Reduced Unwanted Textual Tokens: Ensures a more focused output for coding tasks by minimizing excessive textual responses.

Intended Use

1. Code Generation & Optimization: Designed for developers, assisting in writing, refactoring, and optimizing code across multiple programming languages.
2. Algorithm & Mathematical Problem Solving: Provides precise explanations and solutions for computational and mathematical problems.
3. Technical Explanations & Documentation: Generates clear and structured explanations for coding concepts, libraries, and APIs.
4. Debugging Assistance: Helps analyze code snippets, detect errors, and suggest corrections.
5. Educational Use: Assists students and learners by breaking down complex programming topics into easily understandable sections.
6. Structured Data Processing: Capable of analyzing and generating structured outputs, such as JSON, XML, and tables, making it ideal for data science applications.

Limitations

1. Hardware Requirements: Requires high - memory GPUs or TPUs due to its large parameter size and long - context support.
2. Potential Bias in Responses: While designed to be neutral, outputs may still reflect biases present in training data.
3. Inconsistent Outputs in Creative Tasks: May produce variable results in storytelling and non - technical topics.
4. Limited Real - World Awareness: Does not have access to real - time events beyond its training cutoff.
5. Error Propagation in Extended Outputs: Minor errors in early responses may affect overall coherence in long - form code outputs.
6. Prompt Sensitivity: The effectiveness of responses may depend on how well the input prompt is structured.

📚 Documentation

Model Information

Evaluation Results

Open LLM Leaderboard Evaluation Results Detailed results can be found here! Summarized results can be found here!

📄 License

This model is licensed under the Apache 2.0 license.