license, language, library_name, pipeline_tag, tags, datasets

license	language	library_name	pipeline_tag	tags	datasets
mit	en	transformers	text-generation	llama from-scratch smol	HuggingFaceTB/smol-smoltalk HuggingFaceFW/fineweb_edu_100BT-shuffled

particle-1.0

~100M-parameter Llama-style chat model trained from scratch (random init). Not a fine-tune of Llama, SmolLM, or any Hub base.

Weights are MIT. Training data still needs attribution (below).

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "prathamkode/particle-1.0"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo)

messages = [{"role": "user", "content": "hello"}]
prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
ids = tok(prompt, return_tensors="pt")
out = model.generate(**ids, max_new_tokens=64, temperature=0.7)
print(tok.decode(out[0], skip_special_tokens=False))

Chat format:

<|user|>
hello
<|assistant|>

Model details

Architecture	Llama-style decoder (RoPE, SwiGLU, RMSNorm, tied embeddings)
Parameters	~100M (12 layers, 768 hidden, 12 heads)
Context	2048 tokens
Tokenizer	Custom 32k byte-level BPE (not Llama / GPT-2 vocab)
Init	Random N(0, 0.02) — trained from scratch
Precision	BF16 training; Hub weights bfloat16

Training

1. Tokenizer trained from scratch on a FineWeb-Edu sample (~2GB text).
2. Pretrain next-token prediction on HuggingFaceFW/fineweb_edu_100BT-shuffled, first ~2B tokens.
3. SFT on HuggingFaceTB/smol-smoltalk (first user/assistant turn + a few greeting seeds).

SFT used that dataset as text only. No teacher model weights were copied.

Intended use

Research / demo small chat model. Expect short replies, mistakes, and weak reasoning.

Limitations

• Very small capacity
• May hallucinate
• English-centric FineWeb-Edu subset
• No RLHF / preference tuning

License

• These weights: MIT
• FineWeb-Edu: ODC-By (attribute)
• smol-smoltalk: follow the dataset card