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Training in progress, step 25

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README.md CHANGED
@@ -1,30 +1,59 @@
1
  ---
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  base_model: unsloth/Qwen3.6-35B-A3B
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- library_name: peft
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- tags: [lora, unsloth, qwen3.6, thai, construction-drawing, vlm]
 
 
 
 
 
 
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  ---
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- # Courser_b — t05 "Courser" fold1
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- LoRA adapter สำหรับอ่านแบบก่อสร้าง คสล. ภาษาไทย (โปรเจกต์ Constistant / STECON SS4)
 
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- | | |
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- |---|---|
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- | base | `unsloth/Qwen3.6-35B-A3B` (bf16 LoRA — ตระกูลนี้ quantize ไม่ได้) |
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- | fold | **fold1** จาก k=5 (รัน 4 fold) · train/val = 32/8 บ้าน (4:1) |
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- | train | 1008 แถว — pass1 832 · pass0 92 · pass2.4 69 · pass3 15 |
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- | val | 297 แถว — pass1 188 · pass0 79 · pass2.4 22 · pass3 8 |
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- | LoRA | r=16 α=32 dropout=0 · แปะทุก layer รวม MoE experts ทั้ง 256 |
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- | epochs | 2 · lr 1e-4 cosine · batch 1×8 · adamw_8bit · seed 3407 |
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- | ภาพ | 7,680 visual token/ภาพ (≈7.86 MP) · MAX_LENGTH 47,104 |
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- ## 4 pass ที่เทรนพร้อมกัน
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- - **pass0** จำแนกหน้าแบบ · **pass1** สกัด 7 subtask จากรูปเปล่า
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- - **pass2.4** สกัดพร้อม hint จาก CV · **pass3** ถอดปริมาณจากรูปที่ CV มาร์คแล้ว
24
 
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- ## การแบ่ง fold
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- แบ่งแบบ **stratified ตาม pass** — มีแค่ 10 จาก 40 บ้านที่มี pass0/2.4/3
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- (pass0 มีแค่ 5 บ้าน) ถ้าแบ่งสุ่มธรรมดา val จะไม่มี pass0/pass3 เลยและวัดผลไม่ได้
 
 
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- ⚠️ adapter ตัวเดียวเป็นแค่ 1 ใน 4 fold — **ตัวที่ใช้งานจริงคือ
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- [`dacarokann/destrier`](https://huggingface.co/dacarokann/destrier)** ที่รวมทั้ง 4 fold แล้ว
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  ---
2
  base_model: unsloth/Qwen3.6-35B-A3B
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+ library_name: transformers
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+ model_name: Courser_b
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+ tags:
6
+ - generated_from_trainer
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+ - sft
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+ - unsloth
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+ - trl
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+ licence: license
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  ---
12
 
13
+ # Model Card for Courser_b
14
 
15
+ This model is a fine-tuned version of [unsloth/Qwen3.6-35B-A3B](https://huggingface.co/unsloth/Qwen3.6-35B-A3B).
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+ It has been trained using [TRL](https://github.com/huggingface/trl).
17
 
18
+ ## Quick start
 
 
 
 
 
 
 
 
19
 
20
+ ```python
21
+ from transformers import pipeline
 
22
 
23
+ question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
24
+ generator = pipeline("text-generation", model="dacarokann/Courser_b", device="cuda")
25
+ output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
26
+ print(output["generated_text"])
27
+ ```
28
 
29
+ ## Training procedure
30
+
31
+
32
+
33
+
34
+ This model was trained with SFT.
35
+
36
+ ### Framework versions
37
+
38
+ - TRL: 0.24.0
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+ - Transformers: 5.5.0
40
+ - Pytorch: 2.11.0
41
+ - Datasets: 4.3.0
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+ - Tokenizers: 0.22.2
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+
44
+ ## Citations
45
+
46
+
47
+
48
+ Cite TRL as:
49
+
50
+ ```bibtex
51
+ @misc{vonwerra2022trl,
52
+ title = {{TRL: Transformer Reinforcement Learning}},
53
+ author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
54
+ year = 2020,
55
+ journal = {GitHub repository},
56
+ publisher = {GitHub},
57
+ howpublished = {\url{https://github.com/huggingface/trl}}
58
+ }
59
+ ```
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