Instructions to use prathamkode/particle-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prathamkode/particle-1.0 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="prathamkode/particle-1.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prathamkode/particle-1.0")
model = AutoModelForCausalLM.from_pretrained("prathamkode/particle-1.0", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use prathamkode/particle-1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prathamkode/particle-1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prathamkode/particle-1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prathamkode/particle-1.0

SGLang

How to use prathamkode/particle-1.0 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prathamkode/particle-1.0" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prathamkode/particle-1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prathamkode/particle-1.0" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prathamkode/particle-1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prathamkode/particle-1.0 with Docker Model Runner:
```
docker model run hf.co/prathamkode/particle-1.0
```

particle-1.0

File size: 2,225 Bytes

---
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