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Model: svc-nai-cci/nanollama-public
Source: Original Platform

README.md

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+---
+library_name: transformers
+tags:
+- llama
+- causal-lm
+- text-generation
+- nanollama
+license: apache-2.0
+---
+
+# NanoLlama (Public)
+
+A compact Llama-based language model optimized for efficient inference and deployment. This is the **public** version with open access.
+
+## Model Details
+
+### Model Description
+
+NanoLlama is a small-scale language model based on the Llama architecture, designed for lightweight applications and resource-constrained environments. This model provides a good balance between performance and computational efficiency.
+
+- **Developed by:** svc-nai-cci
+- **Model type:** Causal Language Model
+- **Language(s):** English
+- **License:** Apache 2.0
+- **Finetuned from:** Llama architecture
+- **Access:** Public (Open Access)
+
+### Model Architecture
+
+- **Architecture:** LlamaForCausalLM
+- **Hidden Size:** 4096
+- **Number of Layers:** 4
+- **Number of Attention Heads:** 4
+- **Number of Key-Value Heads:** 2
+- **Vocabulary Size:** 32000
+- **Max Position Embeddings:** 4096
+- **Hidden Activation:** SiLU
+
+## Uses
+
+### Direct Use
+
+This model can be used for:
+- Text generation
+- Conversational AI
+- Code completion
+- Creative writing
+- Question answering
+
+### Downstream Use
+
+The model can be fine-tuned for specific tasks such as:
+- Domain-specific text generation
+- Task-specific instruction following
+- Specialized conversational agents
+
+## How to Get Started with the Model
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load the model and tokenizer
+model_name = "svc-nai-cci/nanollama-public"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Generate text
+input_text = "Hello, how are you?"
+inputs = tokenizer(input_text, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=100, temperature=0.7)
+generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(generated_text)
+```
+
+## Technical Specifications
+
+### Model Architecture and Objective
+
+The model uses the standard Llama architecture with:
+- RMSNorm for layer normalization
+- RoPE (Rotary Position Embedding) for positional encoding
+- SwiGLU activation function
+- Grouped Query Attention (GQA)
+
+### Performance Characteristics
+
+- **Model Size:** Compact design for efficient deployment
+- **Memory Requirements:** Optimized for low-memory environments
+- **Inference Speed:** Fast inference suitable for real-time applications
+
+## Limitations
+
+- Limited context length (4096 tokens)
+- May not perform as well as larger models on complex reasoning tasks
+- Primarily trained/fine-tuned for English text
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{nanollama2024,
+  title={NanoLlama: A Compact Llama-based Language Model},
+  author={svc-nai-cci},
+  year={2024},
+  url={https://huggingface.co/svc-nai-cci/nanollama-public}
+}
+```
+
+## Contact
+
+For questions or issues, please contact: svc-nai-cci