Instructions to use Raghav-Singhal/smollm2-1.7b-100B-linear-merge-epe-no_bce-w0.7-normal-w0.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Raghav-Singhal/smollm2-1.7b-100B-linear-merge-epe-no_bce-w0.7-normal-w0.3 with Transformers:

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

pipe = pipeline("text-generation", model="Raghav-Singhal/smollm2-1.7b-100B-linear-merge-epe-no_bce-w0.7-normal-w0.3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Raghav-Singhal/smollm2-1.7b-100B-linear-merge-epe-no_bce-w0.7-normal-w0.3")
model = AutoModelForCausalLM.from_pretrained("Raghav-Singhal/smollm2-1.7b-100B-linear-merge-epe-no_bce-w0.7-normal-w0.3", 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 Raghav-Singhal/smollm2-1.7b-100B-linear-merge-epe-no_bce-w0.7-normal-w0.3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Raghav-Singhal/smollm2-1.7b-100B-linear-merge-epe-no_bce-w0.7-normal-w0.3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Raghav-Singhal/smollm2-1.7b-100B-linear-merge-epe-no_bce-w0.7-normal-w0.3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Raghav-Singhal/smollm2-1.7b-100B-linear-merge-epe-no_bce-w0.7-normal-w0.3

SGLang

How to use Raghav-Singhal/smollm2-1.7b-100B-linear-merge-epe-no_bce-w0.7-normal-w0.3 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 "Raghav-Singhal/smollm2-1.7b-100B-linear-merge-epe-no_bce-w0.7-normal-w0.3" \
    --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": "Raghav-Singhal/smollm2-1.7b-100B-linear-merge-epe-no_bce-w0.7-normal-w0.3",
		"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 "Raghav-Singhal/smollm2-1.7b-100B-linear-merge-epe-no_bce-w0.7-normal-w0.3" \
        --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": "Raghav-Singhal/smollm2-1.7b-100B-linear-merge-epe-no_bce-w0.7-normal-w0.3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Raghav-Singhal/smollm2-1.7b-100B-linear-merge-epe-no_bce-w0.7-normal-w0.3 with Docker Model Runner:
```
docker model run hf.co/Raghav-Singhal/smollm2-1.7b-100B-linear-merge-epe-no_bce-w0.7-normal-w0.3
```

smollm2-1.7b-100B-linear-merge-epe-no_bce-w0.7-normal-w0.3

Linear weight-merge of two SmolLM2-1.7B pretraining checkpoints (each trained on 100B tokens).

merged = 0.7 * EPE + 0.3 * NORMAL

Component	Weight	Source checkpoint
EPE (no_bce)	0.7	`epe-1p-smollm-1p7b-100B-20n-2048sl-960gbsz-no_bce`
NORMAL	0.3	`normal-smollm-1p7b-100B-20n-2048sl-960gbsz`

Merge details

Method: element-wise linear interpolation of weights (computed in fp32, stored in bf16).
Architecture: LlamaForCausalLM, identical for both parents (hidden 2048, 24 layers, 32 heads, tied embeddings).
Vocabulary: 49280. The two parents share 49152 tokens (interpolated); the 128 extra EPE "charter" tokens are carried over from the EPE parent unchanged. The EPE tokenizer is bundled.

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Safetensors

Model size

2B params

Tensor type

BF16