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Model: CraneAILabs/ganda-gemma-1b-GGUF
Source: Original Platform

.gitattributes

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+*.bin filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+ganda_gemma_ascending_chart.png filter=lfs diff=lfs merge=lfs -text
+ganda_gemma_ascending_clean.png filter=lfs diff=lfs merge=lfs -text

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README.md

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+---
+base_model: google/gemma-3-1b-it
+language:
+- en
+- lg
+library_name: transformers
+license: gemma
+tags:
+- luganda
+- translation
+- conversational
+- gemma
+- gemma3
+- fine-tuned
+pipeline_tag: text-generation
+---
+
+# Ganda Gemma 1B
+
+A fine-tuned Gemma 3 1B instruction model specialized for **English-to-Luganda translation and Luganda conversational AI**. The model accepts input in both English and Luganda but outputs responses exclusively in Luganda.
+
+## 📊 Translation Performance
+
+![Translation Performance Comparison](ganda_gemma_ascending_chart.png)
+
+### Model Comparison
+
+| Model | Parameters | BLEU | chrF++ | Efficiency* |
+|-------|------------|------|--------|-----------|
+| Gemma 3 4B | 4B | 1.1 | 20.05 | 0.28 |
+| Gemma 3 27B | 27B | 3.65 | 31.37 | 0.14 |
+| GPT-5 Mini | N/A | 5.14 | 36.55 | N/A |
+| **Ganda Gemma 1B** | **1B** | **6.99** | **40.32** | **6.99** |
+| Gemini 2.0 Flash | Large | 7.94 | 43.38 | N/A |
+
+
+*Efficiency = BLEU Score ÷ Parameters (in billions)
+
+### Key Performance Insights
+
+🎯 **Efficiency Leader**: Achieves 6.99 BLEU per billion parameters (highest efficiency ratio)  
+🚀 **Size Advantage**: Outperforms Gemma 3 4B (4x larger) by 535% on BLEU score  
+💎 **Competitive Quality**: Achieves similar performance to GPT-5 Mini with known 1B parameter count  
+⚡ **Practical Deployment**: Runs efficiently on consumer hardware while maintaining quality  
+
+### Evaluation Details
+
+- **Dataset**: FLORES-200 English→Luganda (1,012 translation pairs)
+- **Metrics**: BLEU (bilingual evaluation understudy) and chrF++ (character F-score)
+- **Evaluation**: Zero-shot translation performance
+
+
+
+## 🚀 Quick Start
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load model and tokenizer
+model = AutoModelForCausalLM.from_pretrained("CraneAILabs/ganda-gemma-1b")
+tokenizer = AutoTokenizer.from_pretrained("CraneAILabs/ganda-gemma-1b")
+
+# Translate to Luganda
+prompt = "Translate to Luganda: Hello, how are you today?"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=100, temperature=0.3)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+## 🌍 Language Capabilities
+
+- **Input Languages**: English + Luganda
+- **Output Language**: Luganda only
+- **Primary Focus**: English-to-Luganda translation and Luganda conversation
+
+## 🎯 Capabilities
+
+- **Translation**: English-to-Luganda translation
+- **Conversational AI**: Natural dialogue in Luganda
+- **Summarization**: Text summarization in Luganda
+- **Writing**: Creative and informational writing in Luganda
+- **Question Answering**: General knowledge responses in Luganda
+
+## 💻 Usage Examples
+
+### Basic Translation
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("CraneAILabs/ganda-gemma-1b")
+tokenizer = AutoTokenizer.from_pretrained("CraneAILabs/ganda-gemma-1b")
+
+# English to Luganda translation
+prompt = "Translate to Luganda: Welcome to our school"
+inputs = tokenizer(prompt, return_tensors="pt")
+
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_length=100,
+        temperature=0.3,
+        do_sample=True,
+        pad_token_id=tokenizer.eos_token_id
+    )
+
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+### Luganda Conversation
+
+```python
+# Direct Luganda conversation
+prompt = "Oli otya! Osobola okuntuyamba leero?"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=100, temperature=0.3)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+### Using the Pipeline
+
+```python
+from transformers import pipeline
+
+# Create a text generation pipeline
+generator = pipeline(
+    "text-generation",
+    model="CraneAILabs/ganda-gemma-1b",
+    tokenizer="CraneAILabs/ganda-gemma-1b",
+    device=0 if torch.cuda.is_available() else -1
+)
+
+# Generate Luganda text
+result = generator(
+    "Translate to Luganda: Welcome to our school",
+    max_length=100,
+    temperature=0.3,
+    do_sample=True
+)
+print(result[0]['generated_text'])
+```
+
+### Ollama Usage
+
+```bash
+# Run with different quantizations
+ollama run crane-ai-labs/ganda-gemma-1b:q4-k-m    # Recommended balance
+ollama run crane-ai-labs/ganda-gemma-1b:q8-0      # Higher quality
+ollama run crane-ai-labs/ganda-gemma-1b:q4-k-s    # Smaller size
+ollama run crane-ai-labs/ganda-gemma-1b:f16       # Original quality
+```
+
+## 🚀 GGUF Advantages
+
+- **Fast CPU Inference**: Optimized for CPU-only environments
+- **Memory Efficient**: Multiple quantization levels available
+- **Easy Integration**: Works with llama.cpp, Ollama, and other GGUF-compatible tools
+- **Cross-Platform**: Supports Windows, macOS, and Linux
+
+## 📦 Available Quantizations
+
+| Quantization | File Size | Memory Usage | Quality | Use Case |
+|-------------|-----------|--------------|---------|----------|
+| F16         | ~2.0GB    | ~3.5GB      | Best    | GPU inference |
+| Q8_0        | ~1.1GB    | ~2.0GB      | High    | High-end CPU |
+| Q4_K_M      | ~600MB    | ~1.2GB      | Good    | **Recommended** |
+| Q4_K_S      | ~550MB    | ~1.1GB      | Decent  | Low memory |
+| Q4_0        | ~550MB    | ~1.0GB      | Basic   | Very low memory |
+
+## 🔗 Related Models
+
+- **Original Model**: [CraneAILabs/ganda-gemma-1b](https://huggingface.co/CraneAILabs/ganda-gemma-1b) - Full precision HuggingFace model
+- **Mobile (LiteRT)**: [CraneAILabs/ganda-gemma-1b-litert](https://huggingface.co/CraneAILabs/ganda-gemma-1b-litert) - Optimized for Android/iOS
+
+## 📄 License
+
+This model is released under the [Gemma Terms of Use](https://ai.google.dev/gemma/terms). Please review the terms before use.
+
+---
+
+**Built with ❤️ by Crane AI Labs**
+
+*Ganda Gemma - Your helpful Luganda AI companion, optimized for CPU!*

config.json

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+{
+  "_sliding_window_pattern": 6,
+  "architectures": [
+    "Gemma3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attn_logit_softcapping": null,
+  "bos_token_id": 2,
+  "cache_implementation": "hybrid",
+  "eos_token_id": 106,
+  "final_logit_softcapping": null,
+  "head_dim": 256,
+  "hidden_activation": "gelu_pytorch_tanh",
+  "hidden_size": 1152,
+  "initializer_range": 0.02,
+  "intermediate_size": 6912,
+  "layer_types": [
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "model_type": "gemma3_text",
+  "num_attention_heads": 4,
+  "num_hidden_layers": 26,
+  "num_key_value_heads": 1,
+  "pad_token_id": 0,
+  "query_pre_attn_scalar": 256,
+  "rms_norm_eps": 1e-06,
+  "rope_local_base_freq": 10000,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": 512,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.54.1",
+  "unsloth_fixed": true,
+  "unsloth_version": "2025.7.11",
+  "use_cache": true,
+  "vocab_size": 262144
+}

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