80 lines
2.2 KiB
Markdown
80 lines
2.2 KiB
Markdown
---
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license: mit
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language:
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- en
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- llama
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- from-scratch
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- smol
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datasets:
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- HuggingFaceTB/smol-smoltalk
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- HuggingFaceFW/fineweb_edu_100BT-shuffled
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---
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# particle-1.0
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~100M-parameter Llama-style chat model trained **from scratch** (random init). Not a fine-tune of Llama, SmolLM, or any Hub base.
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Weights are MIT. Training data still needs attribution (below).
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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repo = "prathamkode/particle-1.0"
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tok = AutoTokenizer.from_pretrained(repo)
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model = AutoModelForCausalLM.from_pretrained(repo)
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messages = [{"role": "user", "content": "hello"}]
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prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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ids = tok(prompt, return_tensors="pt")
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out = model.generate(**ids, max_new_tokens=64, temperature=0.7)
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print(tok.decode(out[0], skip_special_tokens=False))
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```
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Chat format:
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```
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<|user|>
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hello
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<|assistant|>
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```
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## Model details
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| Architecture | Llama-style decoder (RoPE, SwiGLU, RMSNorm, tied embeddings) |
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| Parameters | ~100M (12 layers, 768 hidden, 12 heads) |
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| Context | 2048 tokens |
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| Tokenizer | Custom 32k byte-level BPE (not Llama / GPT-2 vocab) |
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| Init | Random `N(0, 0.02)` — trained from scratch |
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| Precision | BF16 training; Hub weights `bfloat16` |
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## Training
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1. **Tokenizer** trained from scratch on a FineWeb-Edu sample (~2GB text).
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2. **Pretrain** next-token prediction on [`HuggingFaceFW/fineweb_edu_100BT-shuffled`](https://huggingface.co/datasets/HuggingFaceFW/fineweb_edu_100BT-shuffled), first ~2B tokens.
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3. **SFT** on [`HuggingFaceTB/smol-smoltalk`](https://huggingface.co/datasets/HuggingFaceTB/smol-smoltalk) (first user/assistant turn + a few greeting seeds).
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SFT used that dataset as **text only**. No teacher model weights were copied.
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## Intended use
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Research / demo small chat model. Expect short replies, mistakes, and weak reasoning.
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## Limitations
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- Very small capacity
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- May hallucinate
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- English-centric FineWeb-Edu subset
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- No RLHF / preference tuning
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## License
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- **These weights:** [MIT](LICENSE)
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- **FineWeb-Edu:** ODC-By (attribute)
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- **smol-smoltalk:** follow the dataset card |