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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.
--- |