|
|
--- |
|
|
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 |
|
|
--- |
|
|
 |
|
|
|
|
|
# **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. |
