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prathamkode
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Model Details
Model Provider
prathamkode
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Input Modalities
Output Modalities
Supported Functionality
python
from transformers import AutoModelForCausalLM, AutoTokenizerrepo = "prathamkode/particle-1.6"tokenizer = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo)messages = [{"role": "user", "content": "hello"}]prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt, return_tensors="pt")output = model.generate(**inputs, max_new_tokens=64, do_sample=False)print(tokenizer.decode(output[0], skip_special_tokens=False))
| Architecture | Llama-style decoder (RoPE, SwiGLU, RMSNorm) |
| Parameters | 109.5M |
| Layers / hidden / heads | 12 / 768 / 12 |
| Context | 2048 tokens |
| Tokenizer | Custom byte-level BPE, 32k vocabulary |
| Precision | bfloat16 |
| License | MIT |
The model is trained from random initialization. It is not a fine-tune of Llama, SmolLM, or any other public checkpoint.
The SFT mix is not published. It did not meet the quality bar we set for this release. Particle 1.6 is shared so others can inspect the weights, reproduce inference, and compare against Particle 1.0.
Research, evaluation, and small demos. Suitable for studying from-scratch training at ~100M scale.
Not intended as a production assistant, a source of facts, or a coding model.
If you use these weights, please cite Particle and the public pretraining corpus used for the 1.0 base.