Venatici-Coder-14B-Y.2

Venatici-Coder-14B-Y.2 is built on the Qwen 2.5 14B modality architecture and enhanced through reinforcement learning to deliver advanced capabilities in coding, computational reasoning, and mathematical problem-solving. This model is fine-tuned for developers and data scientists seeking precision, efficiency, and logical coherence in code generation and explanation tasks.

Key Improvements

1. Reinforcement-Learned for Coding Excellence: Fine-tuned via reinforcement learning to optimize structured and context-aware code generation.
2. Advanced Reasoning Engine: Tailored to solve complex algorithmic and mathematical problems with step-by-step logic.
3. Efficient Memory Utilization: Designed to reduce computational overhead, supporting high-throughput environments.
4. Extended Context Support: Accepts up to 128K tokens of input and can generate up to 8K tokens of output, enabling long-form, detailed code and explanations.
5. Precision-Focused Output: Reduces noise by limiting unwanted textual tokens, providing clean and actionable code.

Quickstart with transformers

Here is a Python code snippet using apply_chat_template to load and generate outputs from the model:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Venatici-Coder-14B-Y.2"

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 reasoning-based 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)

Intended Use

1. Code Generation & Refactoring
   Designed to help write, debug, and optimize code across diverse programming languages.

2. Algorithm Design & Math Problem Solving
   Excels in structured logical reasoning, computational tasks, and math-heavy scenarios.

3. Technical Explanation & Learning Aid
   Breaks down complex coding topics, making it ideal for learning and teaching.

4. Debugging & Troubleshooting
   Identifies errors, suggests corrections, and explains root causes.

5. Structured Data Workflows
   Generates and parses structured data formats (JSON, XML, CSV) for data pipelines and API development.

Limitations

1. Hardware Intensive
   Requires high-memory GPU/TPU setups due to its parameter size and extended token limits.

2. Bias Reflection
   May exhibit biases present in the training data, despite reinforcement tuning.

3. Creative Variability
   Not ideal for creative writing or narrative generation.

4. No Real-Time Awareness
   Responses are based on pre-trained knowledge without awareness of recent events.

5. Error Propagation in Long Outputs
   Minor errors can cascade in extended generations.

6. Prompt Sensitivity
   Output quality can depend on how clearly the input is phrased.