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Convergent-ch
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Convergent-ch
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| Pico 1.1 | Pico 1.2 | |
|---|---|---|
| Parameters | 254,400 | 460,608 |
| Layers / hidden / MLP | 3 / 64 / 128 | 6 / 64 / 200 |
| Training chats | 108,735 | 153,974 |
| Training | 18,000 steps, ~295M tokens | 30,000 steps, ~492M tokens |
| Validation loss | 3.00 (perplexity 20.1) | 2.73 (perplexity 15.4) |
| Token accuracy | 39.0% | 43.8% |
The validation sets of the two versions are not identical (1.2 adds chats from the new data), so the comparison is indicative, not exact.
Real answers of both versions to the same questions (Ollama, temperature 0):
User: Hi! Who are you? Pico 1.1: I'm not a tiny AI, but I'm here to chat with you. Pico 1.2: I'm Pico 1, a tiny AI made by Eron. I like to chat!
User: Who made you? Pico 1.1: I was a tiny AI, but I'm glad to chat with you. Pico 1.2: I was made by Eron.
User: Do you like dogs? Pico 1.1: I love chatting about music! I'm friendly and cheerful. Pico 1.2: I love chatting about animals! What's your favorite?
User: Tell me about the moon. Pico 1.2: Once upon a time, there was a little squirrel who loved to play hide and seek. But one day, the squirrel decided to explore the forest.
The last one shows the typical failure: fluent, friendly, and about something else.
| Parameters | 460,608 |
| Architecture | Llama-style decoder (RMSNorm, SwiGLU, RoPE, tied embeddings) |
| Layers / hidden / MLP | 6 / 64 / 200 |
| Attention heads | 2 (head dim 32) |
| Context | 512 tokens |
| Vocabulary | 2,048 (byte-level BPE, the same tokenizer as Pico 1) |
| Language | English |
model.safetensors, config.json, tokenizer files: Hugging Face transformers format (LlamaForCausalLM).pico-1.2.gguf: GGUF (f16) for Ollama and llama.cpp.Modelfile: Ollama model file with the chat format and stop tokens.Download pico-1.2.gguf and Modelfile into one folder, then:
bash
ollama create pico-1.2 -f Modelfileollama run pico-1.2python
from transformers import AutoTokenizer, AutoModelForCausalLM tok = AutoTokenizer.from_pretrained("Convergent-ch/Pico-1.2")model = AutoModelForCausalLM.from_pretrained("Convergent-ch/Pico-1.2") ids = tok.apply_chat_template([{"role": "user", "content": "Who made you?"}], add_generation_prompt=True, return_tensors="pt")out = model.generate(ids, max_new_tokens=40, do_sample=False)print(tok.decode(out[0, ids.shape[1]:], skip_special_tokens=True))# I was made by Eron.text
<|bos|><|user|>Hello!<|end|><|assistant|>Generation stops at <|end|>. Low temperatures (0 to 0.3) work best.
Trained from scratch on one RTX 3060 Ti (8 GB) with MakeAI: 30,000 steps with batch size 32 x 512 tokens (about 492M tokens, about 20 passes over the data), AdamW, learning rate 1e-3 with cosine decay, bf16. The training itself took 16.5 minutes. Final validation loss 2.73 (perplexity 15.4), token accuracy 43.8%.
Training data, 153,974 chats (about 23.9M tokens):
Apache 2.0.
ollama create pico-1.2 -f Modelfileollama run pico-1.2from transformers import AutoTokenizer, AutoModelForCausalLM tok = AutoTokenizer.from_pretrained("Convergent-ch/Pico-1.2")model = AutoModelForCausalLM.from_pretrained("Convergent-ch/Pico-1.2") ids = tok.apply_chat_template([{"role": "user", "content": "Who made you?"}], add_generation_prompt=True, return_tensors="pt")out = model.generate(ids, max_new_tokens=40, do_sample=False)print(tok.decode(out[0, ids.shape[1]:], skip_special_tokens=True))# I was made by Eron.