Viper Coder V1.7 Vsm6

🚀 Viper-Coder-v1.7-Vsm6

Viper-Coder-v1.7-Vsm6 is based on the Qwen 2.5 14B modality architecture, aiming to enhance coding efficiency and computational reasoning. It excels in various technical tasks and is optimized for streamlined memory usage.

🚀 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/Viper-Coder-v1.7-Vsm6"

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

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.

📚 Documentation

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.

📄 License

This project is licensed under the Apache-2.0 license.

📊 Model Information

📈 Evaluation Results

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