marcuscedricridia commited on
Commit
87a72d6
·
verified ·
1 Parent(s): 65f7724

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +56 -6
README.md CHANGED
@@ -9,14 +9,64 @@ tags:
9
  license: apache-2.0
10
  language:
11
  - en
 
 
 
12
  ---
13
 
14
- # Uploaded model
15
 
16
- - **Developed by:** Linggowiktiks
17
- - **License:** apache-2.0
18
- - **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
19
 
20
- This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
21
 
22
- [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
 
9
  license: apache-2.0
10
  language:
11
  - en
12
+ - tl
13
+ datasets:
14
+ - Linggowiktiks/AnoNa
15
  ---
16
 
17
+ # 🦙 Liyama-3B
18
 
19
+ **Liyama-3B** is a fine-tuned version of Meta’s LLaMA-3B (3.2) model, built to understand and respond fluently in **Tagalog**. It was trained on the **AnoNa** dataset over **3 epochs**, aiming for natural, context-aware instruction-following in Filipino.
 
 
20
 
21
+ ---
22
+
23
+ ## 🔤 Origin of the Name
24
+ The name **Liyama** is a Tagalified version of *llama*, reflecting both its LLaMA base and its Tagalog-focused language capabilities. It mirrors how Filipino often adapts foreign terms into familiar, phonetic forms—like *camera → kamera*, *lion → leon*, and now, *llama → liyama*.
25
+
26
+ ---
27
+
28
+ ## 🧠 Training Data: The AnoNa Dataset
29
+
30
+ Liyama-3B was trained solely on **response completions** from the **AnoNa** dataset — a self-instruct corpus generated using **Gemini 1.5** and **2.0**.
31
+
32
+ Inspired by **SimpleQnA**, the dataset contains short, helpful instruction-response pairs. But **AnoNa** introduces several improvements:
33
+
34
+ - ✅ **Less English, More Tagalog** prompts
35
+ - ✅ **Less IFEVAL-style formatting**
36
+ - ✅ **No overuse of modifiers** in instructions
37
+ - ✅ **Balanced task types** to avoid dominant categories
38
+ - ✅ **Complex tasks favored** (65% complex / 35% simple)
39
+ - ✅ **Reduced sycophancy** and generic praise
40
+ - ✅ **Improved follow-up handling**
41
+ - ✅ **AI self-intro appears only when relevant**
42
+ - ✅ **Implicit chain-of-thought reasoning**, not labeled
43
+ - ✅ **Extra task types** added to increase variety
44
+
45
+ This focus creates a model that's practical, straightforward, and tuned for **realistic conversational use in Filipino**, without excessive formatting or irrelevant disclaimers.
46
+
47
+ ---
48
+
49
+ ## 🗣️ Use Case
50
+
51
+ Liyama-3B is ideal for:
52
+ - Answering questions in Tagalog
53
+ - Writing essays, reflections, and letters in Filipino
54
+ - Following natural instructions, even when mixed with English
55
+ - Chat-based tasks where fluency and tone matter
56
+ - Educational or community apps centered around local language use
57
+
58
+ ---
59
+
60
+ ## 📦 Model Details
61
+
62
+ | Feature | Value |
63
+ |--------------------|----------------------------|
64
+ | Base Model | LLaMA-3B v3.2 |
65
+ | Fine-tuned Dataset | AnoNa |
66
+ | Epochs | 3 |
67
+ | Language Focus | Tagalog (with some English)|
68
+ | Prompt Format | Responses only |
69
+
70
+ ---
71
 
72
+ Liyama-3B is part of a broader effort to create open, practical Filipino-language models for real use—not just benchmarks. Expect follow-ups tuned for multi-turn chat, reasoning, and creative tasks.