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