111 lines
4.5 KiB
Markdown
111 lines
4.5 KiB
Markdown
---
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library_name: transformers
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license: apache-2.0
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base_model:
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- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
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pipeline_tag: text-generation
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language:
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- en
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tags:
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- text-generation-inference
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- RL
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- Math
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- Code
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- Reasoning
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---
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# **Fomalhaut-QwenR-1.5B**
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> **Fomalhaut-QwenR-1.5B** is a language model fine-tuned from **DeepSeek-R1-Distilled-Qwen-1.5B** using **distributed reinforcement learning (RL)**. This version enhances capabilities in **mathematical reasoning**, **coding ability**, and **error correction**, delivering efficient general-purpose reasoning and intelligent assistance in a lightweight 1.5B parameter architecture.
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## **Key Improvements**
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1. **Mathematical Reasoning Enhancements**:
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Equipped with advanced capabilities in handling mathematical logic, symbolic computation, step-by-step problem-solving, and numerical accuracy across topics from basic arithmetic to higher-order mathematics.
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2. **Coding and Debugging Proficiency**:
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Improved performance in code generation, understanding documentation, and identifying and correcting bugs in multiple programming languages, especially Python, JavaScript, and C++. It supports functional, object-oriented, and scripting paradigms.
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3. **Intelligent Error Correction**:
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Capable of identifying inconsistencies or errors in logical reasoning, structured formats (JSON, XML), and code outputs, with suggestions and auto-corrections.
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4. **Enhanced Instruction Following**:
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Fine-tuned for following complex, nested instructions with increased precision and coherence over extended prompts and interactions.
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5. **Long-Context Support**:
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Supports up to **128K tokens** for input context and can generate up to **8K tokens** in one output, making it well-suited for extended problem solving, document generation, and analysis.
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## **Quickstart with Transformers**
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "prithivMLmods/Fomalhaut-QwenR-1.5B"
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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 = "Explain the difference between breadth-first search and depth-first search with Python code examples."
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messages = [
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{"role": "system", "content": "You are a knowledgeable assistant skilled in reasoning, coding, and explanation."},
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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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```
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## **Intended Use**
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1. **Mathematics and Computation**:
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Effective for solving math problems, verifying formulas, symbolic logic, algebraic reasoning, and analytical computations.
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2. **Programming Assistance**:
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Ideal for generating, explaining, and debugging code. Suitable for both learning and software development use cases.
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3. **Educational and Informational Support**:
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Provides accurate, well-explained answers to conceptual and applied questions in STEM and humanities.
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4. **Conversational AI and Reasoning Agents**:
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Designed for intelligent chatbots capable of nuanced reasoning, error correction, and structured dialogue.
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5. **Multilingual & Global Applications**:
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Useful for translation, multilingual support bots, and cross-lingual content generation.
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6. **Long-Form & Structured Content Generation**:
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Can create long documents, reports, and structured outputs like JSON, Markdown, and tabular formats.
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## **Limitations**
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1. **Hardware Requirements**:
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While lighter than 14B models, it still benefits from modern GPUs/TPUs for inference due to long-context handling.
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2. **Real-Time Limitations**:
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No real-time awareness; knowledge is limited to training data.
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3. **Bias and Hallucination**:
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While reduced, some bias and hallucinations from training data may persist.
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4. **Creative Consistency**:
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Variability in outputs for creative or ambiguous queries (e.g., fiction, storytelling).
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5. **Prompt Sensitivity**:
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Results may vary significantly depending on the structure and clarity of the input prompt. |