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
```

prathamkode commited on 4 days ago

Commit

1e1c225

verified ·

1 Parent(s): 5c5b094

Update README.md

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README.md +66 -65

README.md CHANGED Viewed

@@ -10,70 +10,71 @@ tags:
 - smol
 datasets:
 - HuggingFaceTB/smol-smoltalk
 ---
-# 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

 - 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