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Model: sartifyllc/Pawa-Gemma-Swahili-2B
Source: Original Platform

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README.md

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+---
+language:
+- sw
+- en
+license: apache-2.0
+base_model:
+- google/gemma-2-2b
+library_name: transformers
+---
+
+# PAWA: Swahili SML for Various Tasks
+
+---
+
+## Overview
+
+**PAWA** is a Swahili-specialized language model designed to excel in tasks requiring nuanced understanding and interaction in Swahili and English. It leverages supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) for improved performance and consistency. Below are the detailed model specifications, installation steps, usage examples, and its intended applications.
+
+---
+### Model Details
+
+- **Model Name**: Pawa-Gemma-Swahili-2B
+- **Model Type**: PAWA  
+- **Architecture**:  
+  - 2B Parameter Gemma-2 Base Model  
+  - Enhanced with Swahili SFT and DPO datasets.  
+- **Languages Supported**:  
+  - Swahili  
+  - English  
+  - Custom tokenizer for multi-language flexibility.  
+- **Primary Use Cases**:  
+  - Contextually rich Swahili-focused tasks.  
+  - General assistance and chat-based interactions.  
+- **License**: Custom/Contact Author for terms of use.  
+
+---
+### Installation and Setup
+Ensure the necessary libraries are installed and up-to-date:
+
+```bash
+!pip uninstall transformers -y && pip install --upgrade --no-cache-dir "git+https://github.com/huggingface/transformers.git"
+!pip uninstall unsloth -y && pip install --upgrade --no-cache-dir "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
+!pip install datasets
+```
+---
+### Model Loading
+You can load the model using the following code snippet:
+
+```python
+from unsloth import FastLanguageModel
+import torch
+
+model_name = "sartifyllc/Pawa-kaggle-gemma-2b"
+max_seq_length = 2048  
+dtype = None  
+load_in_4bit = False  
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name=model_name,
+    max_seq_length=max_seq_length,
+    dtype=dtype,
+    load_in_4bit=load_in_4bit,
+)
+```
+
+---
+### Chat Template Configuration
+For a seamless conversational experience, configure the tokenizer with the appropriate chat template:
+```python
+from unsloth.chat_templates import get_chat_template
+FastLanguageModel.for_inference(model) # Enable native 2x faster inference
+
+tokenizer = get_chat_template(
+    tokenizer,
+    chat_template="chatml",  # Supports templates like zephyr, chatml, mistral, etc.
+    mapping={"role": "from", "content": "value", "user": "human", "assistant": "gpt"},  # ShareGPT style
+    map_eos_token=True,  # Maps <|im_end|> to </s>
+)
+```
+---
+### Usage Example
+Generate a short story in Swahili:
+
+```python
+messages = [{"from": "human", "value": "Tengeneza hadithi fupi"}]
+inputs = tokenizer.apply_chat_template(
+    messages,
+    tokenize=True,
+    add_generation_prompt=True,
+    return_tensors="pt",
+).to("cuda")
+
+from transformers import TextStreamer
+text_streamer = TextStreamer(tokenizer)
+_ = model.generate(input_ids=inputs, streamer=text_streamer, max_new_tokens=128, use_cache=True)
+```
+---
+### Training and Fine-Tuning Details
+
+- **Base Model**: Gemma-2-2B
+- **Continue Pre-Training**: 3B Swahili Tokens
+- **Fine-tuning**: Enhanced with Swahili SFT datasets for improved contextual understanding.  
+- **Optimization**: Includes DPO for deterministic and consistent responses.  
+
+---
+
+### Intended Use Cases
+
+- **General Assistance**:  
+  Provides structured answers for general-purpose use.  
+
+- **Interactive Q&A**:  
+  Designed for general-purpose chat environments.  
+
+- **RAG (Retrieval-Augmented Generation)**:  
+  Works best for RAG and specific use cases.
+
+---
+### Limitations
+
+- **Biases**:  
+  The model may exhibit biases inherent in its fine-tuning datasets.
+
+- **Generalization**:  
+  May struggle with tasks outside the trained domain.
+
+- **Hardware Requirements**:  
+  - Optimal performance requires GPUs with high memory (e.g., Tesla V100 or T4).  
+  - Supports 4-bit quantization for reduced memory usage.
+
+
+Feel free to reach out for further guidance or collaboration opportunities regarding PAWA!

config.json

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+  "_name_or_path": "/home/ai4d/Desktop/Mollel/base_env/model_output/DPO_27_December_2024_11_09_PM",
+  "architectures": [
+    "Gemma2ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attn_logit_softcapping": 50.0,
+  "bos_token_id": 2,
+  "cache_implementation": "hybrid",
+  "eos_token_id": [
+    1,
+    107
+  ],
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+  "hidden_act": "gelu_pytorch_tanh",
+  "hidden_activation": "gelu_pytorch_tanh",
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+  "initializer_range": 0.02,
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+  "max_position_embeddings": 8192,
+  "model_type": "gemma2",
+  "num_attention_heads": 8,
+  "num_hidden_layers": 26,
+  "num_key_value_heads": 4,
+  "pad_token_id": 0,
+  "query_pre_attn_scalar": 256,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.46.3",
+  "unsloth_version": "2024.11.7",
+  "use_cache": true,
+  "vocab_size": 256000
+}

generation_config.json

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+  "bos_token_id": 2,
+  "cache_implementation": "hybrid",
+  "eos_token_id": [
+    1,
+    107
+  ],
+  "max_length": 8192,
+  "pad_token_id": 0,
+  "transformers_version": "4.46.3"
+}

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