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1from transformers import AutoModelForCausalLM, AutoTokenizer
2
3model = AutoModelForCausalLM.from_pretrained("Ringkvist/SmolLM-135M-SLERP-Merge")
4tokenizer = AutoTokenizer.from_pretrained("Ringkvist/SmolLM-135M-SLERP-Merge")
5
6inputs = tokenizer("Explain what photosynthesis is:", return_tensors="pt")
7outputs = model.generate(**inputs, max_new_tokens=100, temperature=0.7)
8print(tokenizer.decode(outputs[0], skip_special_tokens=True))1slices:
2 - sources:
3 - model: HuggingFaceTB/SmolLM-135M
4 layer_range: [0, 30]
5 - model: HuggingFaceTB/SmolLM-135M-Instruct
6 layer_range: [0, 30]
7merge_method: slerp
8base_model: HuggingFaceTB/SmolLM-135M
9parameters:
10 t:
11 - value: 0.6
12dtype: float16| Model | Role | Description |
|---|---|---|
| HuggingFaceTB/SmolLM-135M | Base | Raw language modeling |
| HuggingFaceTB/SmolLM-135M-Instruct | Instruct | Instruction following |