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Model: sartifyllc/Pawa-Gemma-Swahili-2B Source: Original Platform
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README.md
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README.md
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---
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language:
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- sw
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- en
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license: apache-2.0
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base_model:
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- google/gemma-2-2b
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library_name: transformers
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---
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# PAWA: Swahili SML for Various Tasks
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---
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## Overview
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**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.
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---
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### Model Details
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- **Model Name**: Pawa-Gemma-Swahili-2B
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- **Model Type**: PAWA
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- **Architecture**:
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- 2B Parameter Gemma-2 Base Model
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- Enhanced with Swahili SFT and DPO datasets.
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- **Languages Supported**:
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- Swahili
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- English
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- Custom tokenizer for multi-language flexibility.
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- **Primary Use Cases**:
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- Contextually rich Swahili-focused tasks.
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- General assistance and chat-based interactions.
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- **License**: Custom/Contact Author for terms of use.
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---
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### Installation and Setup
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Ensure the necessary libraries are installed and up-to-date:
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```bash
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!pip uninstall transformers -y && pip install --upgrade --no-cache-dir "git+https://github.com/huggingface/transformers.git"
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!pip uninstall unsloth -y && pip install --upgrade --no-cache-dir "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
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!pip install datasets
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```
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---
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### Model Loading
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You can load the model using the following code snippet:
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```python
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from unsloth import FastLanguageModel
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import torch
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model_name = "sartifyllc/Pawa-kaggle-gemma-2b"
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max_seq_length = 2048
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dtype = None
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load_in_4bit = False
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name=model_name,
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max_seq_length=max_seq_length,
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dtype=dtype,
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load_in_4bit=load_in_4bit,
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)
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```
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---
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### Chat Template Configuration
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For a seamless conversational experience, configure the tokenizer with the appropriate chat template:
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```python
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from unsloth.chat_templates import get_chat_template
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FastLanguageModel.for_inference(model) # Enable native 2x faster inference
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tokenizer = get_chat_template(
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tokenizer,
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chat_template="chatml", # Supports templates like zephyr, chatml, mistral, etc.
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mapping={"role": "from", "content": "value", "user": "human", "assistant": "gpt"}, # ShareGPT style
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map_eos_token=True, # Maps <|im_end|> to </s>
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)
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```
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---
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### Usage Example
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Generate a short story in Swahili:
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```python
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messages = [{"from": "human", "value": "Tengeneza hadithi fupi"}]
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inputs = tokenizer.apply_chat_template(
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messages,
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tokenize=True,
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add_generation_prompt=True,
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return_tensors="pt",
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).to("cuda")
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from transformers import TextStreamer
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text_streamer = TextStreamer(tokenizer)
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_ = model.generate(input_ids=inputs, streamer=text_streamer, max_new_tokens=128, use_cache=True)
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```
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---
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### Training and Fine-Tuning Details
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- **Base Model**: Gemma-2-2B
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- **Continue Pre-Training**: 3B Swahili Tokens
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- **Fine-tuning**: Enhanced with Swahili SFT datasets for improved contextual understanding.
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- **Optimization**: Includes DPO for deterministic and consistent responses.
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---
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### Intended Use Cases
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- **General Assistance**:
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Provides structured answers for general-purpose use.
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- **Interactive Q&A**:
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Designed for general-purpose chat environments.
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- **RAG (Retrieval-Augmented Generation)**:
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Works best for RAG and specific use cases.
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---
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### Limitations
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- **Biases**:
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The model may exhibit biases inherent in its fine-tuning datasets.
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- **Generalization**:
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May struggle with tasks outside the trained domain.
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- **Hardware Requirements**:
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- Optimal performance requires GPUs with high memory (e.g., Tesla V100 or T4).
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- Supports 4-bit quantization for reduced memory usage.
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Feel free to reach out for further guidance or collaboration opportunities regarding PAWA!
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config.json
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"architectures": [
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"Gemma2ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"attn_logit_softcapping": 50.0,
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"bos_token_id": 2,
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"cache_implementation": "hybrid",
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"final_logit_softcapping": 30.0,
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"head_dim": 256,
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"sliding_window": 4096,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.46.3",
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"unsloth_version": "2024.11.7",
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"use_cache": true,
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"vocab_size": 256000
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}
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generation_config.json
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"model.layers.9.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.norm.weight": "model-00002-of-00002.safetensors"
|
||||
}
|
||||
}
|
||||
30
special_tokens_map.json
Normal file
30
special_tokens_map.json
Normal file
@@ -0,0 +1,30 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<bos>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<pad>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"unk_token": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:fb108e8f52e4f91587c974585008f1b06cf705d5c459070569788aafdedcc619
|
||||
size 34362877
|
||||
2007
tokenizer_config.json
Normal file
2007
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user