94 lines
1.8 KiB
Markdown
94 lines
1.8 KiB
Markdown
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---
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license: apache-2.0
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language:
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- en
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base_model: HuggingFaceTB/SmolLM2-135M-Instruct
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- smollm2
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- reasoning
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- chain-of-thought
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- instruct
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- lora
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- merged
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datasets:
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- Avtrkrb/combined-reasoning
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---
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# SmolLM2-135M-Instruct-Reasoning
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A reasoning-focused fine-tune of SmolLM2-135M-Instruct trained using Unsloth on the combined-reasoning dataset.
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## Model Overview
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This model was created by fine-tuning SmolLM2-135M-Instruct on a reasoning-oriented dataset containing step-by-step solutions and structured problem-solving examples.
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The model is intended to:
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- Produce detailed reasoning traces
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- Explain intermediate steps
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- Perform multi-step logical reasoning
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- Improve instruction following
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- Generate transparent solutions instead of only final answers
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## Base Model
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HuggingFaceTB/SmolLM2-135M-Instruct
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## Dataset
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Avtrkrb/combined-reasoning
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## Training Method
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- Framework: Unsloth
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- Fine-tuning type: LoRA
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- Task: Supervised Fine-Tuning (SFT)
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## Prompt Format
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This model uses the standard SmolLM2 chat template.
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<|im_start|>system
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You are a helpful AI assistant named SmolLM, trained by Hugging Face
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<|im_end|>
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<|im_start|>user
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What is 17 × 23?
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<|im_end|>
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<|im_start|>assistant
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## Example Usage
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_id = "Avtrkrb/SmolLM2-135M-Instruct-bnb-4bit-reasoning"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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## Intended Use
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- Reasoning tasks
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- Educational explanations
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- Problem solving
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- Step-by-step analysis
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## Limitations
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- Small 135M parameter model
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- Reasoning quality is limited compared to larger models
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- May hallucinate facts
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- Should not be used for high-stakes decisions
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## License
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Apache 2.0 (inherits base model licensing)
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