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Convergent-ch
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Convergent-ch
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python
from transformers import AutoTokenizer, AutoModelForCausalLMrepo = "Convergent-ch/Alpina-Big-2.0-300M"tokenizer = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo)messages = [{"role": "system", "content": "You are a helpful English-speaking assistant. Be clear, accurate, and concise."}, {"role": "user", "content": "Who are you?"}]inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True)output = model.generate(**inputs, max_new_tokens=80, do_sample=False)print(tokenizer.decode(output[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))Research release with very limited evaluation. Identity claims describe the customization and do not change the original model authorship. See evaluation.json for actual generated answers. Do not infer benchmark quality from successful name responses.
Training sources include prefixes of SmolLM-Corpus (ODC-BY), permissively filtered CodeParrot, Smol-SmolTalk (Apache-2.0), and self-oss-instruct-sc2-exec-filter-50k (ODC-BY). See provenance files where supplied. Original dataset notices and licenses apply to the source data; the training datasets are not redistributed here. No affiliation with Qwen or dataset authors is implied.
shell
ollama run girgineron/alpina-big-2.0:300mThe F16 GGUF is also included here. The Modelfile uses the trained chat format with LF newlines. No retraining was performed during conversion.