90 lines
4.5 KiB
Markdown
90 lines
4.5 KiB
Markdown
---
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license: llama3.2
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language:
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- en
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- text-generation-inference
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---
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# **Llama3.2-3B-Grpo-Exp**
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> The **Llama3.2-3B-Grpo-Exp** is a fine-tuned version of the **Llama-3.1-8B** base model, further enhanced with the **GSM8K dataset** for superior text generation and mathematical reasoning. This model is designed for advanced reasoning, structured problem-solving, and contextually rich outputs, making it an excellent choice for applications in **education, programming, research, and creative writing**.
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With its optimized architecture, **Llama3.2-3B-Grpo-Exp** excels at:
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- **Logical reasoning** and **step-by-step problem-solving**
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- **Mathematical and coding tasks**, leveraging specialized expert models
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- **Generating long-form content** (up to 8K tokens) with improved coherence
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- **Understanding structured data**, including tables and JSON outputs
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- **Following instructions** and **adapting to diverse system prompts**, making it ideal for chatbots and AI assistants
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## **Key Features**
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- **Supports long-context processing** of up to **128K tokens**
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- **Fine-tuned using Supervised Fine-Tuning (SFT) and Reinforcement Learning with Human Feedback (RLHF)**
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## **Model Architecture**
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Llama3.2-3B-Grpo-Exp is built on the optimized transformer architecture of **Llama-3.1-8B**, integrating **enhanced dataset logits from GSM8K** for better mathematical reasoning and structured output generation.
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## **Using with transformers**
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To run conversational inference using `transformers >= 4.43.0`, use the `pipeline` abstraction or leverage the `generate()` function with the Auto classes.
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Ensure your environment is updated with:
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```bash
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pip install --upgrade transformers
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```
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### **Example Usage**
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```python
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import torch
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from transformers import pipeline
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model_id = "prithivMLmods/Llama3.2-3B-Grpo-Exp"
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pipe = pipeline(
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"text-generation",
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model=model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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messages = [
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{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
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{"role": "user", "content": "Who are you?"},
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]
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outputs = pipe(
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messages,
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max_new_tokens=256,
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)
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print(outputs[0]["generated_text"][-1])
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```
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## **Intended Use**
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**Llama3.2-3B-Grpo-Exp** is designed for a wide range of applications requiring deep reasoning, structured outputs, and logical text generation. It is particularly suited for:
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- **Education & Research**: Generating detailed explanations, step-by-step solutions, and structured academic content.
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- **Programming & Code Generation**: Assisting in code writing, debugging, and algorithm explanations with improved logic structuring.
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- **AI Chatbots & Assistants**: Providing context-aware, instruction-following responses for conversational AI applications.
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- **Creative Writing**: Generating high-quality stories, articles, and structured narratives with coherence.
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- **Data Analysis & Structured Output Generation**: Interpreting and generating JSON, tables, and formatted outputs for structured data processing.
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## **Limitations**
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While **Llama3.2-3B-Grpo-Exp** is optimized for deep reasoning and structured outputs, it has some limitations:
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1. **Not a Real-time Knowledge Source**
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- The model is trained on a fixed dataset and does not have real-time internet access. It may not provide up-to-date information on rapidly evolving topics.
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2. **Potential Biases**
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- As with all AI models, responses may reflect biases present in the training data. Users should critically evaluate outputs, especially in sensitive domains.
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3. **Mathematical & Logical Reasoning Constraints**
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- While strong in step-by-step reasoning, it may occasionally produce incorrect mathematical calculations or logical inconsistencies. External verification is recommended for critical applications.
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4. **Handling of Extremely Long Contexts**
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- While it supports up to 128K tokens, efficiency and coherence may degrade when processing very long documents or conversations.
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5. **Limited Handling of Ambiguity**
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- The model may struggle with highly ambiguous or context-dependent queries, sometimes generating plausible but incorrect responses.
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6. **Ethical & Compliance Considerations**
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- Not intended for generating misinformation, automating legal or medical decisions, or other high-risk applications without human oversight. |