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Vishwas1
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tinyllm-cpu-char

Text Generation
PyTorch
tinyllm
character-language-model
gpt
cpu
educational
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Add local philosophy TinyLLM checkpoints

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Files changed (8) hide show
  1. README.md +35 -23
  2. checkpoints/tinyllm_philosophy_quotes_local_3k.json +84 -0
  3. checkpoints/tinyllm_philosophy_quotes_local_3k.pt +3 -0
  4. checkpoints/tinyllm_philosophy_quotes_tiny200_4k.json +79 -0
  5. checkpoints/tinyllm_philosophy_quotes_tiny200_4k.pt +3 -0
  6. checkpoints/tinyllm_philosophy_quotes_tiny50_3k.json +66 -0
  7. checkpoints/tinyllm_philosophy_quotes_tiny50_3k.pt +3 -0
  8. data/philosophy_quotes_local_manifest.json +497 -0
README.md CHANGED
@@ -15,56 +15,59 @@ pipeline_tag: text-generation
15
 
16
  A tiny CPU-trained character-level GPT-style language model created as an educational end-to-end LLM experiment.
17
 
18
- This is **not** a general chatbot. It is a deliberately small model trained on a tiny toy corpus to demonstrate the full loop:
19
 
20
  1. build a vocabulary
21
  2. train a causal transformer
22
  3. save a checkpoint
23
  4. generate text from learned character patterns
24
 
25
- ## Model details
26
 
 
 
 
 
 
 
 
27
  - Architecture: tiny GPT-style causal transformer
28
  - Tokenization: character-level
29
  - Parameters: 106,688
30
  - Training device: CPU
31
- - Training steps: 3,000
32
  - Dataset: tiny included toy corpus, `data/tiny_corpus.txt`
33
- - Checkpoint: `checkpoints/tinyllm_overfit_3k.pt`
 
34
 
35
- Final training run:
36
 
37
- ```text
38
- step 0: train 3.6967, val 3.6921
39
- step 3000: train 0.1903, val 3.7094
40
- ```
41
 
42
- The rising validation loss is expected here: this checkpoint intentionally overfits the tiny corpus to prove the training loop works.
 
 
 
 
43
 
44
  ## Quick inference
45
 
46
  ```bash
47
  pip install torch huggingface_hub
48
- python hf_infer.py --repo-id YOUR_USERNAME/tinyllm-cpu-char --prompt "A seed" --tokens 300
 
 
49
  ```
50
 
51
  For local inference after cloning:
52
 
53
  ```bash
54
- python sample.py --ckpt checkpoints/tinyllm_overfit_3k.pt --prompt "The little machine" --tokens 300
 
55
  ```
56
 
57
- Example output after overfitting:
58
 
59
- ```text
60
- A seed does not pretend to be a forest.
61
- A sed shows that a forest is posssible.
62
-
63
- The student asked: can a tiny model think?
64
- The teacher answered: first let it predict...
65
- ```
66
-
67
- ## Training
68
 
69
  ```bash
70
  python train.py --steps 3000 --eval-interval 500 --eval-iters 10 \
@@ -72,9 +75,18 @@ python train.py --steps 3000 --eval-interval 500 --eval-iters 10 \
72
  --out checkpoints/tinyllm_overfit_3k.pt
73
  ```
74
 
 
 
 
 
 
 
 
 
 
75
  ## Limitations
76
 
77
  - Character-level only.
78
- - Trained on a tiny toy corpus.
79
  - Generates memorized/stylized snippets, not reliable knowledge.
 
80
  - Intended for learning and experimentation.
 
15
 
16
  A tiny CPU-trained character-level GPT-style language model created as an educational end-to-end LLM experiment.
17
 
18
+ This is **not** a general chatbot. It is a deliberately small model trained to demonstrate the full loop:
19
 
20
  1. build a vocabulary
21
  2. train a causal transformer
22
  3. save a checkpoint
23
  4. generate text from learned character patterns
24
 
25
+ ## Safety / provenance rule
26
 
27
+ This repo contains checkpoints created locally by us. We did **not** pull external model weights or external quote datasets into the training machine for the philosophy experiments. The philosophy corpus was extracted from existing local workspace philosophy book material, and a provenance manifest is included at `data/philosophy_quotes_local_manifest.json`.
28
+
29
+ ## Checkpoints
30
+
31
+ ### Original toy corpus checkpoint
32
+
33
+ - Checkpoint: `checkpoints/tinyllm_overfit_3k.pt`
34
  - Architecture: tiny GPT-style causal transformer
35
  - Tokenization: character-level
36
  - Parameters: 106,688
37
  - Training device: CPU
 
38
  - Dataset: tiny included toy corpus, `data/tiny_corpus.txt`
39
+ - Training steps: 3,000
40
+ - Final loss: train `0.1903`, val `3.7094`
41
 
42
+ ### Local philosophy quote checkpoints
43
 
44
+ These were trained from local-only quote-style corpora derived from existing workspace philosophy files. They are intentionally tiny and educational.
 
 
 
45
 
46
+ | Checkpoint | Corpus | Params | Final loss | Notes |
47
+ |---|---:|---:|---:|---|
48
+ | `checkpoints/tinyllm_philosophy_quotes_local_3k.pt` | 1200 lines | 111,040 | train `1.9311`, val `1.9449` | Learns broad philosophy texture; not overfit. |
49
+ | `checkpoints/tinyllm_philosophy_quotes_tiny200_4k.pt` | 200 lines | 110,720 | train `1.5459`, val `1.8426` | Stronger style learning; still not memorized. |
50
+ | `checkpoints/tinyllm_philosophy_quotes_tiny50_3k.pt` | 50 lines | 107,840 | train `0.3808`, val `2.5139` | Best overfit-ish quote-style checkpoint. |
51
 
52
  ## Quick inference
53
 
54
  ```bash
55
  pip install torch huggingface_hub
56
+ python hf_infer.py --repo-id Vishwas1/tinyllm-cpu-char \
57
+ --filename checkpoints/tinyllm_philosophy_quotes_tiny50_3k.pt \
58
+ --prompt "Wisdom" --tokens 300
59
  ```
60
 
61
  For local inference after cloning:
62
 
63
  ```bash
64
+ python sample.py --ckpt checkpoints/tinyllm_philosophy_quotes_tiny50_3k.pt \
65
+ --prompt "The soul" --tokens 300
66
  ```
67
 
68
+ ## Training examples
69
 
70
+ Original toy overfit:
 
 
 
 
 
 
 
 
71
 
72
  ```bash
73
  python train.py --steps 3000 --eval-interval 500 --eval-iters 10 \
 
75
  --out checkpoints/tinyllm_overfit_3k.pt
76
  ```
77
 
78
+ Local philosophy tiny50 overfit-ish run:
79
+
80
+ ```bash
81
+ python train.py --data data/philosophy_quotes_local_tiny50.txt \
82
+ --steps 3000 --eval-interval 500 --eval-iters 10 \
83
+ --batch-size 16 --n-embd 64 --block-size 64 --lr 0.001 \
84
+ --out checkpoints/tinyllm_philosophy_quotes_tiny50_3k.pt
85
+ ```
86
+
87
  ## Limitations
88
 
89
  - Character-level only.
 
90
  - Generates memorized/stylized snippets, not reliable knowledge.
91
+ - Weird spelling and malformed words are expected.
92
  - Intended for learning and experimentation.
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