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Convergent-ch
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Convergent-ch
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| Parameters | 100,685,568 |
| Architecture | Llama-style decoder (RMSNorm, SwiGLU, RoPE, tied embeddings, grouped-query attention) |
| Layers / hidden / MLP | 14 / 768 / 2048 |
| Attention heads | 12 query heads, 4 key/value heads (head dim 64) |
| Context | 2,048 tokens |
| Vocabulary | 16,384 (byte-level BPE) |
| Language | English |
model.safetensors, config.json, tokenizer files: Hugging Face transformers format (LlamaForCausalLM).cobalt-1.gguf: GGUF (f16) for Ollama and llama.cpp.Modelfile: Ollama model file with the chat format, stop tokens and recommended settings.Download cobalt-1.gguf and Modelfile into one folder, then:
bash
ollama create cobalt-1 -f Modelfileollama run cobalt-1Use the Modelfile: without it Ollama guesses a wrong chat format and the model produces nonsense. It runs on a Raspberry Pi 5 and on any normal computer; on an RTX 3060 Ti it generates about 600-700 tokens per second.
python
from transformers import AutoTokenizer, AutoModelForCausalLM tok = AutoTokenizer.from_pretrained("Convergent-ch/Cobalt-1")model = AutoModelForCausalLM.from_pretrained("Convergent-ch/Cobalt-1") ids = tok.apply_chat_template([{"role": "user", "content": "What is the capital of Japan?"}], add_generation_prompt=True, return_tensors="pt")out = model.generate(ids, max_new_tokens=60, do_sample=False)print(tok.decode(out[0, ids.shape[1]:], skip_special_tokens=True))# The capital of Japan is Tokyo.text
<|bos|><|user|>Hello!<|end|><|assistant|>Generation stops at <|end|>. Low temperatures (0 to 0.3) and a light repetition penalty (about 1.15) work best.
Same questions, temperature 0, both in Ollama with their own Modelfile:
| Question | Tavik-1 (20M) | Cobalt-1 (100M) |
|---|---|---|
| What is the capital of Japan? | "...is Joshua, a city located in the northern part of the country." | "The capital of Japan is Tokyo." |
| What is the largest planet in our solar system? | "...is Europa, a small, icy region..." | Rambles with made-up numbers, but names Jupiter. |
| Give me three tips to study better. | Partly sensible list. | Sensible list (practice regularly, take breaks, ...). |
| Who made you? | "Convergent created me." | "Convergent created me. I'm Cobalt." |
| Why do we have seasons on Earth? | Wrong. | Wrong. |
| Write a short haiku about the ocean. | Prose, not a haiku. | Prose, not a haiku. |
| What is 17 times 23? | Nonsense. | Nonsense. |
Cobalt-1 is clearly more fluent and gets more simple facts right, but explanations, poetry forms and arithmetic are still beyond it.
All stages ran on one RTX 3060 Ti (8 GB) with MakeAI, bf16, AdamW, cosine schedule, 262,144 tokens per optimizer step.
Chat data:
The validation losses of different models are not comparable when their tokenizers differ (Tavik-1 uses 8,192 tokens, Cobalt-1 16,384).
Apache 2.0.
ollama create cobalt-1 -f Modelfileollama run cobalt-1from transformers import AutoTokenizer, AutoModelForCausalLM tok = AutoTokenizer.from_pretrained("Convergent-ch/Cobalt-1")model = AutoModelForCausalLM.from_pretrained("Convergent-ch/Cobalt-1") ids = tok.apply_chat_template([{"role": "user", "content": "What is the capital of Japan?"}], add_generation_prompt=True, return_tensors="pt")out = model.generate(ids, max_new_tokens=60, do_sample=False)print(tok.decode(out[0, ids.shape[1]:], skip_special_tokens=True))# The capital of Japan is Tokyo.