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Model: prithivMLmods/Acrux-500M-o1-Journey
Source: Original Platform

README.md

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+---
+license: creativeml-openrail-m
+datasets:
+- GAIR/o1-journey
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-0.5B-Instruct
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- Qwen2.5
+- Llama-Cpp
+- CoT
+- o1-journey
+- text-generation-inference
+- safetensors
+- Ollama
+---
+### Acrux-500M-o1-Journey Model Files
+
+The **Acrux-500M-o1-Journey** is a lightweight, instruction-tuned language model fine-tuned from the **Qwen2.5-0.5B-Instruct** base model. With a size of 500 million parameters, it is designed for **cost-effective deployment** and **fast text generation** while maintaining quality performance for instruction-following tasks.
+
+| **File Name**             | **Size**       | **Description**                           | **Upload Status**  |
+|----------------------------|----------------|-------------------------------------------|--------------------|
+| `.gitattributes`           | 1.57 kB        | Git attributes for managing LFS files.    | Uploaded           |
+| `README.md`                | 195 Bytes      | Model overview or documentation.          | Updated            |
+| `added_tokens.json`        | 657 Bytes      | Custom tokens for the tokenizer.          | Uploaded           |
+| `config.json`              | 859 Bytes      | Model configuration file.                 | Uploaded           |
+| `generation_config.json`   | 280 Bytes      | Configuration for text generation.        | Uploaded           |
+| `merges.txt`               | 1.82 MB        | Merge rules for byte-pair encoding (BPE). | Uploaded           |
+| `pytorch_model.bin`        | 988 MB         | Model weights (PyTorch format).           | Uploaded (LFS)     |
+| `special_tokens_map.json`  | 644 Bytes      | Mapping for special tokens.               | Uploaded           |
+| `tokenizer.json`           | 11.4 MB        | Full tokenizer configuration.             | Uploaded (LFS)     |
+| `tokenizer_config.json`    | 7.73 kB        | Additional tokenizer settings.            | Uploaded           |
+| `vocab.json`               | 2.78 MB        | Vocabulary for the tokenizer.             | Uploaded           |
+### **Key Features:**
+
+1. **Compact Size with Efficient Performance:**  
+   The smaller parameter count (500M) ensures faster inference and reduced hardware requirements.
+
+2. **Instruction Optimization:**  
+   Fine-tuned to follow prompts effectively, making it suitable for interactive applications and prompt-based tasks.
+
+3. **Domain-Specific Training:**  
+   Trained on the **GAIR/o1-journey** dataset, providing tailored capabilities for specific use cases.
+
+---
+
+### **Training Details:**
+- **Base Model:** [Qwen2.5-0.5B-Instruct](#)
+- **Dataset Used for Fine-Tuning:** [GAIR/o1-journey](#)  
+  - A compact dataset focusing on instruction-driven generation with 1.42k samples.
+
+---
+### **Capabilities:**
+
+1. **Instruction Following:**  
+   - Generates accurate and coherent responses to user instructions.
+   - Handles summarization, question-answering, and conversational tasks.
+
+2. **Fast Inference:**  
+   - Ideal for real-time applications due to reduced latency from its smaller size.
+
+3. **Interactive AI Development:**  
+   - Suitable for chatbots, virtual assistants, and instructional interfaces.
+
+---
+### **Usage Instructions:**
+
+1. **Setup:**  
+   Download all model files, ensuring compatibility with the Hugging Face Transformers library.
+
+2. **Loading the Model:**  
+   ```python
+   from transformers import AutoModelForCausalLM, AutoTokenizer
+   
+   model_name = "prithivMLmods/Acrux-500M-o1-Journey"
+   tokenizer = AutoTokenizer.from_pretrained(model_name)
+   model = AutoModelForCausalLM.from_pretrained(model_name)
+   ```
+3. **Sample Generate Text:**  
+   ```python
+   input_text = "Explain the concept of machine learning in simple terms."
+   inputs = tokenizer(input_text, return_tensors="pt")
+   outputs = model.generate(**inputs, max_length=100, temperature=0.7)
+   print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+   ```
+4. **Optimize Generation:**  
+   Adjust parameters in `generation_config.json` for better control of output, such as:
+   - `temperature` for randomness.
+   - `top_p` for sampling diversity.
+   - `max_length` for output size.
+---