101 lines
3.8 KiB
Markdown
101 lines
3.8 KiB
Markdown
---
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library_name: transformers
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pipeline_tag: text-generation
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license: apache-2.0
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language:
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- en
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- zh
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base_model:
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- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
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tags:
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- text-generation-inference
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- Code
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- Math
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- RL
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- R1
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---
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# **Primus-Optima-QwenKV-1.54B**
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> **Primus-Optima-QwenKV-1.54B** is an **experimental chain-of-thought reasoning and code generation model**, built by combining the strengths of two sources:
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>
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> - **DeepSeek R1 (distilled 1.5B)** for strong math and coding reasoning.
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> - **Qwen2.5-0.5B**, fine-tuned with **Process Reward Models (PRM)** to boost structured step-by-step outputs in math and logic.
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This hybrid design results in a **bilingual, high-precision model** with enhanced **reasoning depth**, **multi-step clarity**, and **lightweight adaptability** for math and code applications.
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## **Key Features**
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1. **Chain-of-Thought Reasoning for Math + Code**
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Designed to produce human-like intermediate steps in both math and programming problems — useful for education, tutoring, and technical assistants.
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2. **Hybrid Architecture (Reasoning + Reward-Guided Fine-Tuning)**
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Combines **DeepSeek R1’s** distilled capabilities with **Qwen2.5-0.5B**'s reward-optimized reasoning for structured, goal-driven outputs.
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3. **Multilingual Capabilities (English + 中文)**
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Fluent and accurate in both English and Simplified Chinese, making it suitable for diverse learning and development environments.
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4. **Coder Experimental Mode**
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Able to solve algorithmic tasks, complete functions, and offer code walkthroughs using the same step-by-step format as it does for math.
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5. **Lightweight Yet Capable (1.54B)**
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With just 1.54B parameters, it is efficient for local deployments while offering surprisingly strong performance on STEM and programming tasks.
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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/Primus-Optima-QwenKV-1.54B"
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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 compute factorial using recursion."
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messages = [
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{"role": "system", "content": "You are an expert tutor in math and programming, explaining step-by-step."},
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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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- **Math & Programming Tutors**: Assist students with logic-driven step-by-step explanations.
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- **Bilingual STEM Apps**: Ideal for dual-language math or coding environments.
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- **Competitive Reasoning Tools**: Suited for reasoning-intensive tasks like Olympiad prep, technical quizzes, and programming challenges.
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- **On-Device LLMs**: Lightweight enough for web or embedded applications needing real-time reasoning.
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## **Limitations**
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1. **Experimental Nature**:
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This is a hybrid research model; performance may vary across general or creative domains.
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2. **Size Constraints**:
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As a 1.54B parameter model, extremely complex reasoning tasks may challenge its capabilities.
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3. **Bias & Generalization**:
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Inherits biases from both DeepSeek R1 and Qwen2.5. Use caution in high-stakes or sensitive applications.
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4. **Prompt Engineering Required**:
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Structured prompts with clear questions yield the best results, especially for multi-step problems. |