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Model: prithivMLmods/Fomalhaut-QwenR-1.5B
Source: Original Platform

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model:
+- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
+pipeline_tag: text-generation
+language:
+- en
+tags:
+- text-generation-inference
+- RL
+- Math
+- Code
+- Reasoning
+---
+
+![r.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/Tx6eUgqfe4Qd3Cu4kQd0w.png)
+
+# **Fomalhaut-QwenR-1.5B**
+
+> **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.
+
+## **Key Improvements**
+1. **Mathematical Reasoning Enhancements**:  
+   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.
+
+2. **Coding and Debugging Proficiency**:  
+   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.
+
+3. **Intelligent Error Correction**:  
+   Capable of identifying inconsistencies or errors in logical reasoning, structured formats (JSON, XML), and code outputs, with suggestions and auto-corrections.
+
+4. **Enhanced Instruction Following**:  
+   Fine-tuned for following complex, nested instructions with increased precision and coherence over extended prompts and interactions.
+
+5. **Long-Context Support**:  
+   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.
+
+## **Quickstart with Transformers**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Fomalhaut-QwenR-1.5B"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Explain the difference between breadth-first search and depth-first search with Python code examples."
+messages = [
+    {"role": "system", "content": "You are a knowledgeable assistant skilled in reasoning, coding, and explanation."},
+    {"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]
+```
+
+## **Intended Use**
+
+1. **Mathematics and Computation**:  
+   Effective for solving math problems, verifying formulas, symbolic logic, algebraic reasoning, and analytical computations.
+
+2. **Programming Assistance**:  
+   Ideal for generating, explaining, and debugging code. Suitable for both learning and software development use cases.
+
+3. **Educational and Informational Support**:  
+   Provides accurate, well-explained answers to conceptual and applied questions in STEM and humanities.
+
+4. **Conversational AI and Reasoning Agents**:  
+   Designed for intelligent chatbots capable of nuanced reasoning, error correction, and structured dialogue.
+
+5. **Multilingual & Global Applications**:  
+   Useful for translation, multilingual support bots, and cross-lingual content generation.
+
+6. **Long-Form & Structured Content Generation**:  
+   Can create long documents, reports, and structured outputs like JSON, Markdown, and tabular formats.
+
+## **Limitations**
+
+1. **Hardware Requirements**:  
+   While lighter than 14B models, it still benefits from modern GPUs/TPUs for inference due to long-context handling.
+
+2. **Real-Time Limitations**:  
+   No real-time awareness; knowledge is limited to training data.
+
+3. **Bias and Hallucination**:  
+   While reduced, some bias and hallucinations from training data may persist.
+
+4. **Creative Consistency**:  
+   Variability in outputs for creative or ambiguous queries (e.g., fiction, storytelling).
+
+5. **Prompt Sensitivity**:  
+   Results may vary significantly depending on the structure and clarity of the input prompt.