Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- microsoft/orca-math-word-problems-200k
+- ise-uiuc/Magicoder-Evol-Instruct-110K
+- Vezora/Tested-22k-Python-Alpaca
+---
+
+# Datacard for Custom Trained Model
+- Base Model : [Kukedlc/NeuralExperiment-7b-dare-ties](https://huggingface.co/Kukedlc/NeuralExperiment-7b-dare-ties)
+
+  
+## Model Description
+This model is an experimental AI trained on three distinct datasets focusing on logical reasoning, mathematics, and programming. The training process involved fine-tuning from the last layer (31) backward with a gradually decreasing learning rate. The primary goal is to address and rectify the common 'INSTINST' bug observed in leaderboard models through targeted training on the latest layers.
+
+## Datasets Used for Training
+- `microsoft/orca-math-word-problems-200k`: A large-scale dataset of mathematical word problems aimed at enhancing the model's numerical reasoning and problem-solving capabilities.
+- `ise-uiuc/Magicoder-Evol-Instruct-110K`: A dataset designed to improve code generation and understanding, contributing to the model's programming language proficiency.
+- `sahil2801/CodeAlpaca-20k`: A dataset focused on programming challenges to further refine the model's coding and logical reasoning skills.
+
+Each dataset contributed 20,000 data points to the training process, ensuring a balanced representation of logic, mathematics, and programming tasks.
+
+## Training Environment
+- The model was trained on Kaggle's free GPU environment, allowing for cost-effective fine-tuning and experimentation.
+- Users interested in replicating or extending this training can find the Kaggle notebook in my profile or request it directly for collaborative purposes.
+
+## Preliminary Results
+- The model shows promising results in solving logical puzzles and mathematical problems, especially those with misleading or non-obvious solutions that it initially struggled with.
+- Ongoing experiments aim to quantify the impact of targeted training on the model's reasoning capabilities across different domains.
+
+## Invitation for Collaboration
+- Feedback, suggestions, and collaborative efforts are highly encouraged to further refine and evaluate the model.
+- If interested in contributing or experimenting with this model, please feel free to reach out or access the code directly from my Kaggle profile.
+
+## Contact Information
+- For any inquiries, suggestions, or collaboration proposals, please contact me!
+
+```python
+!pip install -qU transformers accelerate
+
+from transformers import AutoTokenizer
+import transformers
+import torch
+
+model = "Kukedlc/NeuralExperiment-7b-MagicCoder-v7"
+messages = [{"role": "user", "content": "What is a large language model?"}]
+
+tokenizer = AutoTokenizer.from_pretrained(model)
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+
+outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
+print(outputs[0]["generated_text"])
+```
+
+![Kukedlc/NeuralExperiment-7b-dare-ties](https://raw.githubusercontent.com/kukedlc87/imagenes/main/DALL%C2%B7E%202024-03-05%2000.28.41%20-%20Imagine%20a%20visual%20representation%20of%20a%20language%20model%20inspired%20by%20the%20Mandelbrot%20fractal.%20The%20scene%20should%20depict%20an%20abstract%2C%20intricate%20network%20resembl.webp)