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Model: prithivMLmods/Primus-Optima-QwenKV-1.54B
Source: Original Platform

README.md

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+---
+library_name: transformers
+pipeline_tag: text-generation
+license: apache-2.0
+language:
+- en
+- zh
+base_model:
+- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
+tags:
+- text-generation-inference
+- Code
+- Math
+- RL
+- R1
+---
+![KV.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/7WWuZljYRluVp5gi3--9a.png)
+
+# **Primus-Optima-QwenKV-1.54B**
+
+> **Primus-Optima-QwenKV-1.54B** is an **experimental chain-of-thought reasoning and code generation model**, built by combining the strengths of two sources:
+>
+> - **DeepSeek R1 (distilled 1.5B)** for strong math and coding reasoning.
+> - **Qwen2.5-0.5B**, fine-tuned with **Process Reward Models (PRM)** to boost structured step-by-step outputs in math and logic.
+
+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.
+
+## **Key Features**
+
+1. **Chain-of-Thought Reasoning for Math + Code**  
+   Designed to produce human-like intermediate steps in both math and programming problems — useful for education, tutoring, and technical assistants.
+
+2. **Hybrid Architecture (Reasoning + Reward-Guided Fine-Tuning)**  
+   Combines **DeepSeek R1’s** distilled capabilities with **Qwen2.5-0.5B**'s reward-optimized reasoning for structured, goal-driven outputs.
+
+3. **Multilingual Capabilities (English + 中文)**  
+   Fluent and accurate in both English and Simplified Chinese, making it suitable for diverse learning and development environments.
+
+4. **Coder Experimental Mode**  
+   Able to solve algorithmic tasks, complete functions, and offer code walkthroughs using the same step-by-step format as it does for math.
+
+5. **Lightweight Yet Capable (1.54B)**  
+   With just 1.54B parameters, it is efficient for local deployments while offering surprisingly strong performance on STEM and programming tasks.
+
+## **Quickstart with Transformers**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Primus-Optima-QwenKV-1.54B"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Write a Python function to compute factorial using recursion."
+messages = [
+    {"role": "system", "content": "You are an expert tutor in math and programming, explaining step-by-step."},
+    {"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**
+
+- **Math & Programming Tutors**: Assist students with logic-driven step-by-step explanations.
+- **Bilingual STEM Apps**: Ideal for dual-language math or coding environments.
+- **Competitive Reasoning Tools**: Suited for reasoning-intensive tasks like Olympiad prep, technical quizzes, and programming challenges.
+- **On-Device LLMs**: Lightweight enough for web or embedded applications needing real-time reasoning.
+
+## **Limitations**
+
+1. **Experimental Nature**:  
+   This is a hybrid research model; performance may vary across general or creative domains.
+
+2. **Size Constraints**:  
+   As a 1.54B parameter model, extremely complex reasoning tasks may challenge its capabilities.
+
+3. **Bias & Generalization**:  
+   Inherits biases from both DeepSeek R1 and Qwen2.5. Use caution in high-stakes or sensitive applications.
+
+4. **Prompt Engineering Required**:  
+   Structured prompts with clear questions yield the best results, especially for multi-step problems.