Instructions to use prathamkode/particle-2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prathamkode/particle-2.0 with Transformers:

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

pipe = pipeline("text-generation", model="prathamkode/particle-2.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prathamkode/particle-2.0")
model = AutoModelForCausalLM.from_pretrained("prathamkode/particle-2.0", 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-2.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prathamkode/particle-2.0"
# 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-2.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prathamkode/particle-2.0

SGLang

How to use prathamkode/particle-2.0 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-2.0" \
    --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-2.0",
		"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-2.0" \
        --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-2.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prathamkode/particle-2.0 with Docker Model Runner:
```
docker model run hf.co/prathamkode/particle-2.0
```

Particle 2.0

Particle 2.0 is a compact (~100M) chat model trained from scratch. It uses the same architecture as Particle 1.0.

This release is a further train plus a supervised fine-tune on a new dataset mix. Training data is not published.

Weights are released under MIT.

Quick start

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "prathamkode/particle-2.0"
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))

Model


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.

Training

Continued training and a supervised fine-tune on a new dataset mix. The mix is not published.

Intended use

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.

Limitations

Small capacity: weak on reasoning, long context, and tools
Can hallucinate or contradict itself
English-centric
No preference tuning

Citation

If you use these weights, please cite Particle.

Downloads last month: -

Safetensors

Model size

0.1B params

Tensor type

BF16

prathamkode
/

particle-2.0