---
license: mit
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- llama
- from-scratch
- smol
datasets:
- 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

```python
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`](https://huggingface.co/datasets/HuggingFaceFW/fineweb_edu_100BT-shuffled), first ~2B tokens.
3. **SFT** on [`HuggingFaceTB/smol-smoltalk`](https://huggingface.co/datasets/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](LICENSE)
- **FineWeb-Edu:** ODC-By (attribute)
- **smol-smoltalk:** follow the dataset card