104 lines
3.8 KiB
Markdown
104 lines
3.8 KiB
Markdown
---
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license: apache-2.0
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tags:
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- text-generation-inference
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- Coder
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- RL
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- Math
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- Y.2
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- code
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library_name: transformers
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language:
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- en
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base_model:
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- Qwen/Qwen2.5-14B-Instruct
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pipeline_tag: text-generation
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---
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# **Venatici-Coder-14B-Y.2**
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> **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.
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## **Key Improvements**
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1. **Reinforcement-Learned for Coding Excellence**: Fine-tuned via reinforcement learning to optimize structured and context-aware code generation.
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2. **Advanced Reasoning Engine**: Tailored to solve complex algorithmic and mathematical problems with step-by-step logic.
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3. **Efficient Memory Utilization**: Designed to reduce computational overhead, supporting high-throughput environments.
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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.
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5. **Precision-Focused Output**: Reduces noise by limiting unwanted textual tokens, providing clean and actionable code.
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## **Quickstart with transformers**
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Here is a Python code snippet using `apply_chat_template` to load and generate outputs from the model:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "prithivMLmods/Venatici-Coder-14B-Y.2"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = "Write a Python function to find the Fibonacci sequence."
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messages = [
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{"role": "system", "content": "You are an advanced reasoning-based coding assistant."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=512
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print(response)
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```
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## **Intended Use**
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1. **Code Generation & Refactoring**
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Designed to help write, debug, and optimize code across diverse programming languages.
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2. **Algorithm Design & Math Problem Solving**
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Excels in structured logical reasoning, computational tasks, and math-heavy scenarios.
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3. **Technical Explanation & Learning Aid**
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Breaks down complex coding topics, making it ideal for learning and teaching.
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4. **Debugging & Troubleshooting**
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Identifies errors, suggests corrections, and explains root causes.
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5. **Structured Data Workflows**
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Generates and parses structured data formats (JSON, XML, CSV) for data pipelines and API development.
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## **Limitations**
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1. **Hardware Intensive**
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Requires high-memory GPU/TPU setups due to its parameter size and extended token limits.
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2. **Bias Reflection**
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May exhibit biases present in the training data, despite reinforcement tuning.
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3. **Creative Variability**
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Not ideal for creative writing or narrative generation.
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4. **No Real-Time Awareness**
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Responses are based on pre-trained knowledge without awareness of recent events.
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5. **Error Propagation in Long Outputs**
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Minor errors can cascade in extended generations.
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6. **Prompt Sensitivity**
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Output quality can depend on how clearly the input is phrased. |