59 lines
1.6 KiB
Markdown
59 lines
1.6 KiB
Markdown
---
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license: mit
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base_model: google/gemma-2b
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tags:
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- text-generation-inference
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- transformers
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- gemma
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- mini-gemma
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- agentic-ai
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model_type: gemma
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pipeline_tag: text-generation
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---
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# Mini-Gemma Custom Model
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This repository contains a custom domain-specialized fine-tune of the Gemma architecture, optimized for specific text distributions and patterns. The model was trained using the Hugging Face `Trainer` on an accelerated NVIDIA GPU cluster.
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## 📊 Training Performance & Metrics
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The model successfully converged over its training run with highly stable gradients:
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* **Total Training Steps:** 20,000
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* **Final Total Train Loss:** `3.478`
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* **Final Step Loss:** `2.988`
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* **Gradient Norm Stability:** Stable at `~1.12`
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* **Training Status:** Complete / Fully Converged
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## 🚀 Quick Start & Usage
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You can easily load and run this model locally using the Transformers library:
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
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model_id = "agentbyumer/mini-gemma"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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device_map="auto"
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)
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generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
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prompt = "Your specialized prompt here"
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outputs = generator(
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prompt,
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max_new_tokens=150,
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do_sample=True,
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temperature=0.7,
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return_full_text=False
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)
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print(outputs[0]['generated_text'])
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```
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## 📜 License
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This project is licensed under the permissive MIT License. See the accompanying [LICENSE](./LICENSE) file for full details. |