Instructions to use prathamkode/particle-1.6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prathamkode/particle-1.6 with Transformers:

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

pipe = pipeline("text-generation", model="prathamkode/particle-1.6")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prathamkode/particle-1.6")
model = AutoModelForCausalLM.from_pretrained("prathamkode/particle-1.6", 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-1.6 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prathamkode/particle-1.6"
# 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-1.6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prathamkode/particle-1.6

SGLang

How to use prathamkode/particle-1.6 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-1.6" \
    --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-1.6",
		"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-1.6" \
        --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-1.6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prathamkode/particle-1.6 with Docker Model Runner:
```
docker model run hf.co/prathamkode/particle-1.6
```

Particle 1.6

Particle 1.6 is a compact (~100M) chat model trained from scratch. It uses the same architecture and pretrained base as Particle 1.0.

This release applies a second supervised fine-tune on an internal instruction dataset. That pass did not improve the model as much as expected. Everyday chat still works; factual reliability and consistency remain below what we wanted for a general-purpose assistant.

Weights are released under MIT. Training data is not included.

Quick start

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "prathamkode/particle-1.6"
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

Pretrain — ~2B tokens of public educational web text (same base as Particle 1.0).
Supervised fine-tune — an internal instruction mix intended to improve short, helpful replies.

The SFT mix is not published. It did not meet the quality bar we set for this release. Particle 1.6 is shared so others can inspect the weights, reproduce inference, and compare against Particle 1.0.

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 or safety alignment beyond the SFT mix
The additional SFT pass did not deliver the expected lift over 1.0

Citation

If you use these weights, please cite Particle and the public pretraining corpus used for the 1.0 base.

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Safetensors

Model size

0.1B params

Tensor type

BF16

prathamkode
/

particle-1.6