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Model: Norod78/SmolLM-135M-FakyPedia-EngHeb Source: Original Platform
2026-06-25 23:43:23 +08:00
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+---
+library_name: transformers
+license: apache-2.0
+language:
+- en
+- he
+widget:
+- text: <|endoftext|>\%Hugging face
+- text: <|endoftext|>\%Machine learning
+- text: <|endoftext|>\%Wikipedia
+- text: <|endoftext|>\%דורון אדלר
+- text: <|endoftext|>\%
+datasets:
+- wikimedia/wikipedia
+---
+
+# SmolLM-135M-FakyPedia-EngHeb
+
+## Table of Contents
+- [Model Details](#model-details)
+- [Uses](#uses)
+- [Risks, Limitations and Biases](#risks-limitations-and-biases)
+- [Training](#training)
+
+## Model Details
+
+**Base Model**
+
+This model extended the tokenizer of and is a fine-tuned of [SmolLM-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM-135M-Instruct)  
+
+**Model Description:**
+
+A bilingual (English and Hebrew) nonsense generation model which produces silly Wikipedia-like abstract text. 
+
+- **Fine tuned by:** [Doron Adler](https://linktr.ee/Norod78)
+- **Model Type:** Text Generation
+- **Language(s):** English, Hebrew
+- **License:** apache-2.0 (as a derived work of SmolLM)
+
+## Uses
+
+### Input format
+
+BOS-TOKEN followed by '\\%' followed by the optional title for the fake "Wikipedia" article
+
+### Generation
+```bash
+pip install transformers
+```
+
+```python
+# pip install transformers
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+model_id = "Norod78/SmolLM-135M-FakyPedia-EngHeb"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+tokenizer.pad_token_id = tokenizer.eos_token_id
+bos_token = tokenizer.bos_token
+model = AutoModelForCausalLM.from_pretrained(model_id).to(device)
+model.generation_config.pad_token_id = tokenizer.pad_token_id
+
+torch.manual_seed(1234)
+
+def generate_fakypedia(article_title: str):
+    with torch.no_grad():
+        result = ""
+        string_to_tokenize= f"{bos_token}\\%{article_title}"
+        input_ids = tokenizer( string_to_tokenize, return_tensors="pt").input_ids.to(device)
+        sample_outputs = model.generate(input_ids, do_sample=True,repetition_penalty=1.05, top_k = 40, top_p = 0.950, temperature=0.80, max_length=192, num_return_sequences=3)
+        #sample_outputs = model.generate(input_ids, do_sample=True,repetition_penalty=1.2, temperature=0.5, max_length=192, num_return_sequences=3)        
+        if article_title == None or len(article_title) == 0:
+            result += f"# Fakypedia results with random titles  \n"
+            article_title = ""
+        else:
+            result += f"# Fakypedia results for \"{article_title}\"  \n"
+        for i, sample_output in enumerate(sample_outputs):
+            decoded_output = tokenizer.decode(sample_output, skip_special_tokens=True)
+            decoded_output = decoded_output.replace(f"\%{article_title}", f"## {i+1}. {article_title}").replace("\%", " ").replace("\\n", "  \n")
+            decoded_output = decoded_output.replace("##   \n", "\n")
+            result += "{}\n".format(decoded_output)
+        return result
+
+generate_fakypedia("Hugging Face")
+```
+
+### Generate with llama.cpp
+
+Download [SmolLM-135M-FakyPedia-EngHeb-BF16.gguf](https://huggingface.co/Norod78/SmolLM-135M-FakyPedia-EngHeb/resolve/main/SmolLM-135M-FakyPedia-EngHeb-BF16.gguf)  
+Run:
+```bash
+llama-cli -m SmolLM-135M-FakyPedia-EngHeb-BF16.gguf -p "<|endoftext|>\\%Hugging Face"
+```
+
+#### Misuse and Out-of-scope Use
+
+## Risks, Limitations and Biases
+**CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.**
+
+This model is basically a joke and intended to generate silly and fake results. 
+
+## Training
+
+#### Training Data
+ [English and Hebrew Wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia)
+
+#### Training Procedure
+
+* A tokenizer with vocab size of 14,000 was trained
+* The trained tokenizer was then [merged](https://huggingface.co/Norod78/gpt2-tokenizer-with-added-hebrew-14k) at the end of the base model's tokenizer using [this script](https://github.com/huggingface/tokenizers/issues/690#issuecomment-830665989) so the original base model knowledge was retained as well as make it better fine-tunable upon Hebrew text
+* Hebrew and English datasets were [interleaved](https://huggingface.co/docs/datasets/en/process#interleave) so each language had an identical amount of samples.
+* Each example was processed in the following manner:
+```python
+def add_prefix(example):
+  example["text"] = ("\%" + example["title"] + "\%\n" + example["text"]).replace("\n", "\\n")
+  return example
+```