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Model: ai-nexuz/llama-3.2-1b-instruct-fine-tuned
Source: Original Platform

README.md

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+---
+license: apache-2.0
+datasets:
+- kanhatakeyama/wizardlm8x22b-logical-math-coding-sft
+base_model:
+- unsloth/Llama-3.2-1B-Instruct
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- llm
+- maths
+- coding
+- reasoning
+- tech
+- unsloth
+- trl
+- sft
+---
+ 
+
+# LLaMA-3.2-1B-Instruct Fine-Tuned Model
+
+**Model Card for Hugging Face Repository**
+
+---
+
+## Model Summary
+
+This is a fine-tuned version of the **LLaMA-3.2-1B-Instruct** model. Fine-tuned using the `kanhatakeyama/wizardlm8x22b-logical-math-coding-sft` dataset, this model is specialized in **logical reasoning**, **mathematical problem-solving**, and **coding tasks**. Training was performed using **Unsloth** on Google Colab, optimized for performance and usability.
+
+---
+
+## Model Details
+
+- **Model Name**: LLaMA-3.2-1B-Instruct (Fine-tuned)
+- **Base Model**: LLaMA-3.2-1B-Instruct
+- **Fine-Tuning Dataset**: `kanhatakeyama/wizardlm8x22b-logical-math-coding-sft`
+- **Fine-Tuning Framework**: Unsloth
+- **Parameters**: 1 Billion
+- **Domain**: Logical Reasoning, Mathematics, Coding
+- **Tags**: `llama`, `fine-tuning`, `instruction-following`, `math`, `coding`, `logical-reasoning`, `unsloth`
+
+---
+
+## Fine-Tuning Dataset
+
+The fine-tuning dataset, `kanhatakeyama/wizardlm8x22b-logical-math-coding-sft`, is curated for advanced reasoning tasks. It contains:
+- Logical reasoning scenarios
+- Step-by-step mathematical solutions
+- Complex code generation and debugging examples
+
+**Dataset Link**: [kanhatakeyama/wizardlm8x22b-logical-math-coding-sft](https://huggingface.co/datasets/kanhatakeyama/wizardlm8x22b-logical-math-coding-sft)
+
+---
+
+## Intended Use
+
+This model is ideal for tasks such as:
+1. **Logical Problem Solving**: Derive conclusions and explanations for logical questions.
+2. **Mathematics**: Solve algebra, calculus, and other mathematical problems.
+3. **Coding**: Generate, debug, and explain programming code in various languages.
+4. **Instruction-Following**: Handle user queries with clear and concise answers.
+
+### Example Applications:
+- AI tutors
+- Logical reasoning assistants
+- Math-solving bots
+- Code generation and debugging tools
+
+---
+
+## Usage
+
+### Installation
+
+To use this model, install the required dependencies:
+```bash
+pip install transformers datasets torch accelerate
+```
+
+### Loading the Model
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load the fine-tuned model and tokenizer
+model_name = "ai-nexuz/llama-3.2-1b-instruct-fine-tuned"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+```
+
+### Generating Outputs
+
+```python
+prompt = "Solve this equation: 2x + 3 = 7. Find x."
+
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=100)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+print(response)
+```
+
+---
+
+ 
+
+## Model Training
+
+### Hardware
+- **Platform**: Google Colab Pro
+- **GPU**: NVIDIA Tesla T4
+
+### Training Configuration
+- **Batch Size**: 32
+- **Epochs**: 1
+ 
+### Frameworks Used
+- **Unsloth**: For efficient training
+- **Hugging Face Transformers**: For model and tokenizer handling
+
+---
+
+## Limitations
+
+While this model is highly proficient in logical reasoning, mathematics, and coding tasks, there are some limitations:
+- May produce inaccurate results for ambiguous or poorly-defined prompts.
+- Performance may degrade for highly specialized or niche coding languages.
+
+---
+
+## Deployment
+
+### Using Gradio for Web UI
+
+```bash
+pip install gradio
+```
+
+```python
+import gradio as gr
+
+def generate_response(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(**inputs, max_length=200)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+gr.Interface(fn=generate_response, inputs="text", outputs="text").launch()
+```
+
+### Hugging Face Inference API
+This model can also be accessed using the Hugging Face Inference API for hosted deployment:
+```python
+from transformers import pipeline
+
+pipe = pipeline("text-generation", model="ai-nexuz/llama-3.2-1b-instruct-fine-tuned")
+result = pipe("Explain the concept of recursion in programming.")
+print(result)
+```
+
+---
+
+## Acknowledgements
+
+This fine-tuning work was made possible by:
+- **Hugging Face** for their exceptional library and dataset hosting.
+- **Unsloth** for providing an efficient fine-tuning framework.
+- **Google Colab** for GPU resources.
+
+---
+
+## Citation
+
+If you use this model in your research or project, please cite it as:
+```
+@model{llama31b_instruct_finetuned,
+  title={Fine-Tuned LLaMA-3.2-1B-Instruct},
+  author={Your Name},
+  year={2024},
+  url={https://huggingface.co/your-huggingface-repo/llama-3.2-1b-instruct-finetuned},
+}
+```
+
+---
+
+## Licensing
+
+This model is released under the **Apache 2.0 License**. See `LICENSE` for details.
+
+---
+
+**Tags**:  
+`llama` `fine-tuning` `math` `coding` `logical-reasoning` `instruction-following` `transformers`  
+
+**Summary**:  
+A fine-tuned version of LLaMA-3.2-1B-Instruct specializing in logical reasoning, math problem-solving, and code generation. Perfect for AI-driven tutoring, programming assistance, and logical problem-solving tasks.