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Model: Ma7ee7/SmolLM2-135M-Reasoning-5K
Source: Original Platform

README.md

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+---
+base_model: HuggingFaceTB/SmolLM2-135M-Instruct
+datasets:
+- SupraLabs/reasoning-corpus-4K-5M-v1
+library_name: transformers
+pipeline_tag: text-generation
+license: apache-2.0
+language:
+- en
+tags:
+- smollm2
+- reasoning
+- supervised-fine-tuning
+- chat
+- transformers
+- safetensors
+---
+
+# SmolLM2-135M Reasoning-5K
+
+A full-parameter reasoning fine-tune of
+[`HuggingFaceTB/SmolLM2-135M-Instruct`](https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct) using 5,000 examples
+sampled from [`SupraLabs/reasoning-corpus-4K-5M-v1`](https://huggingface.co/datasets/SupraLabs/reasoning-corpus-4K-5M-v1).
+
+The model was trained to place its reasoning trace inside `<think>` and
+`</think>` tags, followed by a separate final answer.
+
+## Training summary
+
+| Setting | Value |
+|---|---:|
+| Training examples | 5,000 |
+| Evaluation examples | 128 |
+| Epochs | 2 |
+| Maximum sequence length | 4,096 tokens |
+| Learning rate | 3e-05 |
+| Training objective | Assistant-only causal cross-entropy |
+| Parameter training | Full model |
+| Precision | bfloat16/float16 depending on training GPU |
+
+The system and user portions were masked from the loss. Samples exceeding the
+maximum context length were rejected instead of being cut through the middle of
+a reasoning trace.
+
+## Usage
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "YOUR_USERNAME/SmolLM2-135M-Reasoning-5K"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype="auto",
+    device_map="auto",
+)
+
+messages = [
+    {
+        "role": "system",
+        "content": 'You are a helpful AI assistant. For difficult problems, reason carefully inside <think> and </think> tags, then provide a clear final answer.',
+    },
+    {
+        "role": "user",
+        "content": "A farmer has 17 sheep. All but 9 run away. How many remain?",
+    },
+]
+
+inputs = tokenizer.apply_chat_template(
+    messages,
+    tokenize=True,
+    add_generation_prompt=True,
+    return_tensors="pt",
+    return_dict=True,
+).to(model.device)
+
+with torch.inference_mode():
+    output = model.generate(
+        **inputs,
+        max_new_tokens=256,
+        do_sample=False,
+        repetition_penalty=1.05,
+    )
+
+new_tokens = output[0, inputs["input_ids"].shape[1]:]
+print(tokenizer.decode(new_tokens, skip_special_tokens=False))
+```
+
+## Reasoning format
+
+The expected assistant format is:
+
+```text
+<think>
+Internal reasoning trace
+</think>
+
+Final answer
+```
+
+This small model is experimental. It should not be assumed to produce correct
+reasoning merely because it emits a structured reasoning trace.
+
+## Files
+
+`training_info.json` records the training configuration, any metrics found in
+the local output directory, and SHA-256 hashes of the uploaded weight files.
+
+## License
+
+The model follows the Apache 2.0 license used by the base SmolLM2 model. Review
+the base model repository and source dataset for their complete terms.