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Ma7ee7
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Model Details
Model Provider
Ma7ee7
Model Tree
HuggingFaceTB/SmolLM2-135M-Instruct
Input Modalities
Output Modalities
Supported Functionality
| 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.
python
import torchfrom 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))The expected assistant format is:
text
<think>Internal reasoning trace</think> Final answerThis small model is experimental. It should not be assumed to produce correct reasoning merely because it emits a structured reasoning trace.
training_info.json records the training configuration, any metrics found in
the local output directory, and SHA-256 hashes of the uploaded weight files.
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.
import torchfrom 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))<think>Internal reasoning trace</think>Final answer