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| Component | Specification |
|---|---|
| Parameters | ~4.9B |
| Layers | 24 transformer blocks |
| Hidden Size | 4,080 |
| Attention Heads | 24 query / 8 key-value (GQA) |
| Context Length | 4,096 tokens |
| Vocabulary Size | 151,665 |
1from transformers import AutoModelForCausalLM, AutoTokenizer
2import torch
3
4model = AutoModelForCausalLM.from_pretrained(
5 "PursuitOfDataScience/Argonne-2.0",
6 torch_dtype=torch.bfloat16,
7 device_map="auto",
8 trust_remote_code=True
9)
10tokenizer = AutoTokenizer.from_pretrained("PursuitOfDataScience/Argonne-2.0", trust_remote_code=True)
11
12prompt = "The future of AI is"
13inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
14outputs = model.generate(**inputs, max_length=256, do_sample=True, temperature=0.7)
15print(tokenizer.decode(outputs[0], skip_special_tokens=True))1@misc{argonne2,
2 author = {PursuitOfDataScience},
3 title = {Argonne 2.0: A 4.9B Parameter Language Model},
4 year = {2026},
5 publisher = {Hugging Face},
6 url = {https://huggingface.co/PursuitOfDataScience/Argonne-2.0}
7}