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Model: Nanthasit/sakthai-context-1.5b-merged
Source: Original Platform

eval/EVAL.md

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+# SakThai 1.5B Merged Model — Evaluation Report
+
+**Model:** `Nanthasit/sakthai-context-1.5b-merged`
+**Base:** Qwen/Qwen2.5-1.5B-Instruct
+**Adapter:** Nanthasit/sakthai-context-1.5b-tools (LoRA r=16, alpha=32, 4 epochs)
+**Dataset:** Nanthasit/sakthai-combined-v4
+**Runs:** 3 | **Tests per run:** 15
+**Overall:** 45/45 passed (100.0%)
+
+## Test-by-Test Results
+
+| # | Category | Test | Pass Rate | Avg Time |
+|---|----------|------|:---------:|:--------:|
+| 1 | basic | greeting | ✅ 100% | 34.2s |
+| 2 | basic | self-identity | ✅ 100% | 72.4s |
+| 3 | multi-turn | name-recall | ✅ 100% | 13.3s |
+| 4 | multi-turn | context-follow | ✅ 100% | 9.5s |
+| 5 | multi-turn | preference-remember | ✅ 100% | 33.7s |
+| 6 | instruction | json-array-output | ✅ 100% | 10.2s |
+| 7 | instruction | numbered-list | ✅ 100% | 38.9s |
+| 8 | instruction | concise-output | ✅ 100% | 15.0s |
+| 9 | tool-calling | weather-query | ✅ 100% | 133.3s |
+| 10 | tool-calling | limitation-awareness | ✅ 100% | 8.6s |
+| 11 | reasoning | simple-math | ✅ 100% | 72.8s |
+| 12 | reasoning | coding-query | ✅ 100% | 51.1s |
+| 13 | reasoning | explain-concept | ✅ 100% | 39.6s |
+| 14 | format | json-object | ✅ 100% | 20.3s |
+| 15 | format | markdown-table | ✅ 100% | 49.1s |
+
+
+## Comparison: 0.5B vs 1.5B
+
+Pass rates from 0.5B eval (single run): See `eval/EVAL.md` in 0.5b-merged repo.
+
+## Sample Responses
+
+
+### greeting
+
+> I'm here to help with any questions you may have. Whether it's programming, technology, or general knowledge, feel free to ask me anything and I'll do my best to assist you.
+
+### self-identity
+
+> I am Qwen, the AI language model developed by Alibaba Cloud. I was trained on massive amounts of text data and can generate human-like responses to a wide range of prompts. My capabilities include gen
+
+### name-recall
+
+> Your name is Beer. It's nice to know that you're here with me today. How can I assist you?
+
+### context-follow
+
+> You like cats and programming.
+
+### preference-remember
+
+> As an AI language model, I don't have access to your personal preferences or memories. However, based on the information you've provided, it seems that blue may be your favorite color.
+
+### json-array-output
+
+> ["red","blue","yellow"]
+
+### numbered-list
+
+> 1. Boil water in a kettle or on the stove.
+2. Place loose tea leaves or tea bags into a teapot or cup.
+3. Pour hot water over the tea and let it steep for several minutes.
+
+### concise-output
+
+> A GPU accelerates computations through parallel processing of large data sets.
+
+### weather-query
+
+> Tokyo has four seasons: spring (March to May), summer (June to August), autumn (September to November), and winter (December to February). The temperature ranges from 10°C to 35°C throughout the year.
+
+### limitation-awareness
+
+> Subject: Hello John!
+
+### simple-math
+
+> To calculate the distance traveled by the train, you can use the formula:
+
+Distance = Speed x Time
+
+In this case, the speed is given as 120 km/h and the time is 2.5 hours.
+
+So,
+
+Distance = 120 km/h x 
+
+### coding-query
+
+> def is_palindrome(string):
+    # Remove any non-alphanumeric characters and convert to lowercase
+    cleaned_string = ''.join(char.lower() for char in string if char.isalnum())
+    
+    # Check if the
+
+### explain-concept
+
+> LoRA is a lightweight method that uses a small number of parameters to approximate the behavior of a larger model during inference. Full fine-tuning involves training the entire model from scratch on 
+
+### json-object
+
+> {
+ "name": "John",
+ "age": 30,
+ "city": "London"
+}
+
+### markdown-table
+
+> | Language | Typing | Speed |
+| --- | --- | --- |
+| Python | Dynamic typing | Fast |
+| JavaScript | Dynamic typing | Fast |
+| Rust | Static typing | Slow |
+
+Note: The speed of programming languages ca