94 lines
4.1 KiB
Markdown
94 lines
4.1 KiB
Markdown
---
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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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--- |