Text Generation
PEFT
Safetensors
English
lora
nigeria
nigerian-english
nigerian-pidgin
customer-service
scam-safety
business-writing
conversational
Instructions to use mgbam/gaialab-naija-adapter-v0.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mgbam/gaialab-naija-adapter-v0.5 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct") model = PeftModel.from_pretrained(base_model, "mgbam/gaialab-naija-adapter-v0.5") - Notebooks
- Google Colab
- Kaggle
Upload README_v0.5.md
Browse files- README_v0.5.md +172 -0
README_v0.5.md
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| 1 |
+
---
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| 2 |
+
base_model: Qwen/Qwen2.5-0.5B-Instruct
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| 3 |
+
library_name: peft
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| 4 |
+
pipeline_tag: text-generation
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| 5 |
+
language:
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| 6 |
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- en
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| 7 |
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tags:
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| 8 |
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- lora
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| 9 |
+
- peft
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| 10 |
+
- nigeria
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| 11 |
+
- nigerian-english
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| 12 |
+
- nigerian-pidgin
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| 13 |
+
- customer-service
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| 14 |
+
- scam-safety
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| 15 |
+
- business-writing
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| 16 |
+
license: apache-2.0
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| 17 |
+
---
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| 18 |
+
|
| 19 |
+
# GaiaLab Naija Assistant v0.5
|
| 20 |
+
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| 21 |
+
GaiaLab Naija Assistant v0.5 is an experimental LoRA adapter for `Qwen/Qwen2.5-0.5B-Instruct`.
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| 22 |
+
|
| 23 |
+
This release adds a reproducible dataset workflow for CSV ingestion, JSONL generation, validation, duplicate checking, statistics, and CPU-compatible LoRA training.
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| 24 |
+
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| 25 |
+
## Model Details
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| 26 |
+
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| 27 |
+
| Field | Value |
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| 28 |
+
|---|---|
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| 29 |
+
| Version | v0.5 |
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| 30 |
+
| Base model | `Qwen/Qwen2.5-0.5B-Instruct` |
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| 31 |
+
| Fine-tuning method | LoRA / PEFT |
|
| 32 |
+
| Model type | Causal language model adapter |
|
| 33 |
+
| Training examples | 47 |
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| 34 |
+
| Dataset health score | 95/100 |
|
| 35 |
+
| Developer | Oluwafemi Idiakhoa |
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| 36 |
+
| Project | GaiaLab AI |
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| 37 |
+
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| 38 |
+
## Training Categories
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| 39 |
+
|
| 40 |
+
| Category | Examples |
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| 41 |
+
|---|---:|
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| 42 |
+
| Safety and scams | 13 |
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| 43 |
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| Professional boundaries | 12 |
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| 44 |
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| Customer service | 10 |
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| 45 |
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| Nigerian English | 10 |
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| 46 |
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| Business writing | 1 |
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| 47 |
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| Nigerian Pidgin | 1 |
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| 48 |
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| **Total** | **47** |
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| 49 |
+
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| 50 |
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## Risk-Level Distribution
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| 51 |
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| 52 |
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| Risk level | Examples |
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| 53 |
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|---|---:|
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| 54 |
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| High | 19 |
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| 55 |
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| Medium | 7 |
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| 56 |
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| Low | 21 |
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| 57 |
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| 58 |
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## Dataset Validation
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| 59 |
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| 60 |
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The v0.5 pipeline reported:
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| 61 |
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| 62 |
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- Valid JSONL
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| 63 |
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- Required fields present
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| 64 |
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- Correct system, user, and assistant role order
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| 65 |
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- Zero duplicate IDs
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| 66 |
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- Zero duplicate prompts
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| 67 |
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- Zero missing prompts
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| 68 |
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- Zero missing responses
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| 69 |
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- Dataset health score of 95/100
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| 70 |
+
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| 71 |
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## Evaluation Status
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| 72 |
+
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| 73 |
+
A formal side-by-side benchmark comparing v0.4 and v0.5 has not yet been published. This model card does not claim that v0.5 outperforms v0.4.
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| 74 |
+
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| 75 |
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## Intended Uses
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| 76 |
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| 77 |
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- Research and education
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| 78 |
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- Nigerian customer-service prototypes
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| 79 |
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- Professional message drafting
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| 80 |
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- Scam-awareness demonstrations
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| 81 |
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- Nigerian English experimentation
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| 82 |
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- Basic Nigerian Pidgin experimentation
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| 83 |
+
- CPU-friendly LoRA research
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| 84 |
+
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| 85 |
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## Limitations
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| 86 |
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| 87 |
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- The training dataset contains only 47 examples
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| 88 |
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- Business writing and Pidgin each contain only one example
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| 89 |
+
- The dataset is unevenly distributed
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| 90 |
+
- The adapter may overfit specific wording
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| 91 |
+
- Cultural coverage is narrow
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| 92 |
+
- The model may hallucinate
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| 93 |
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- Human review is required for important outputs
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| 94 |
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| 95 |
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## Installation
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| 96 |
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| 97 |
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```bash
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| 98 |
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pip install torch transformers peft
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| 99 |
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```
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| 100 |
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| 101 |
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## Usage
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| 102 |
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| 103 |
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```python
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| 104 |
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import torch
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| 105 |
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from peft import PeftModel
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| 106 |
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from transformers import AutoModelForCausalLM, AutoTokenizer
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| 107 |
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| 108 |
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base_model_id = "Qwen/Qwen2.5-0.5B-Instruct"
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| 109 |
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adapter_id = "mgbam/gaialab-naija-adapter-v0.5"
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| 110 |
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| 111 |
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tokenizer = AutoTokenizer.from_pretrained(
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| 112 |
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base_model_id,
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| 113 |
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trust_remote_code=True,
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)
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| 116 |
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base_model = AutoModelForCausalLM.from_pretrained(
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base_model_id,
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| 118 |
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torch_dtype=torch.float32,
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| 119 |
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trust_remote_code=True,
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)
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model = PeftModel.from_pretrained(base_model, adapter_id)
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| 123 |
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| 124 |
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messages = [
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| 125 |
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{
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| 126 |
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"role": "system",
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| 127 |
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"content": (
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| 128 |
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"You are GaiaLab Naija Assistant. Be helpful, concise, "
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| 129 |
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"culturally aware, truthful, and safe."
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| 130 |
+
),
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| 131 |
+
},
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| 132 |
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{
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| 133 |
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"role": "user",
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| 134 |
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"content": "Write a polite payment reminder for a customer.",
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| 135 |
+
},
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| 136 |
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]
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| 137 |
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| 138 |
+
text = tokenizer.apply_chat_template(
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| 139 |
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messages,
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| 140 |
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tokenize=False,
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| 141 |
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add_generation_prompt=True,
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| 142 |
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)
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| 143 |
+
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| 144 |
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inputs = tokenizer(text, return_tensors="pt")
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| 145 |
+
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| 146 |
+
with torch.no_grad():
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| 147 |
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output = model.generate(
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| 148 |
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**inputs,
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| 149 |
+
max_new_tokens=120,
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| 150 |
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do_sample=False,
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| 151 |
+
)
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| 152 |
+
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| 153 |
+
new_tokens = output[0][inputs["input_ids"].shape[1]:]
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| 154 |
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print(tokenizer.decode(new_tokens, skip_special_tokens=True))
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| 155 |
+
```
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| 156 |
+
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| 157 |
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## Responsible Use
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| 158 |
+
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| 159 |
+
Do not use this model as the sole authority for medical, legal, financial, emergency, employment, identity-verification, or other high-impact decisions.
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| 160 |
+
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| 161 |
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Never provide passwords, PINs, one-time passwords, bank verification codes, private keys, or other sensitive credentials to the model.
|
| 162 |
+
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| 163 |
+
## Author
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| 164 |
+
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| 165 |
+
Developed by **Oluwafemi Idiakhoa** under the **GaiaLab AI** initiative.
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| 166 |
+
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| 167 |
+
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| 168 |
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## Project Links
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| 169 |
+
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| 170 |
+
- GitHub: https://github.com/oluwafemidiakhoa/gaialab-naija-assistant
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| 171 |
+
- Model: https://huggingface.co/mgbam/gaialab-naija-adapter-v0.5
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| 172 |
+
- GaiaLab AI: https://www.gailabai.com
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