SmolLM-135M-FakyPedia-EngHeb

README.md

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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|>\\%"
+---
+
+# 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:**
+
+The model developer notes that the model is
+> A Bi-Language 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():  
+        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=1.2, temperature=0.5, max_length=96, num_return_sequences=3)        
+        print(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).replace(f"\%{article_title}", f"## {article_title}").replace("\%", " ").replace("\\n", "  \n")
+            print("{}\n".format(decoded_output))
+
+generate_fakypedia("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
+```

config.json

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+{
+  "_name_or_path": "Norod78/SmolLM-135M-HebEng-Wiki",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "eos_token_id": 0,
+  "hidden_act": "silu",
+  "hidden_size": 576,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "max_position_embeddings": 2048,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 9,
+  "num_hidden_layers": 30,
+  "num_key_value_heads": 3,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.42.3",
+  "use_cache": true,
+  "vocab_size": 62366
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 0,
+  "transformers_version": "4.42.3"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6eef84ca621ae8ec4ed26a01f465f658731cccd22abf04aee480c171bf71ea81
+size 568535464

special_tokens_map.json

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+{
+  "additional_special_tokens": [
+    "<|endoftext|>",
+    "<|im_start|>",
+    "<|im_end|>",
+    "<repo_name>",
+    "<reponame>",
+    "<file_sep>",
+    "<filename>",
+    "<gh_stars>",
+    "<issue_start>",
+    "<issue_comment>",
+    "<issue_closed>",
+    "<jupyter_start>",
+    "<jupyter_text>",
+    "<jupyter_code>",
+    "<jupyter_output>",
+    "<jupyter_script>",
+    "<empty_output>"
+  ],
+  "bos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<repo_name>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "<reponame>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "5": {
+      "content": "<file_sep>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "6": {
+      "content": "<filename>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "7": {
+      "content": "<gh_stars>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "8": {
+      "content": "<issue_start>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "9": {
+      "content": "<issue_comment>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "10": {
+      "content": "<issue_closed>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "11": {
+      "content": "<jupyter_start>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "12": {
+      "content": "<jupyter_text>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "13": {
+      "content": "<jupyter_code>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "14": {
+      "content": "<jupyter_output>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "15": {
+      "content": "<jupyter_script>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "16": {
+      "content": "<empty_output>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<|endoftext|>",
+    "<|im_start|>",
+    "<|im_end|>",
+    "<repo_name>",
+    "<reponame>",
+    "<file_sep>",
+    "<filename>",
+    "<gh_stars>",
+    "<issue_start>",
+    "<issue_comment>",
+    "<issue_closed>",
+    "<jupyter_start>",
+    "<jupyter_text>",
+    "<jupyter_code>",
+    "<jupyter_output>",
+    "<jupyter_script>",
+    "<empty_output>"
+  ],
+  "bos_token": "<|endoftext|>",
+  "chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": null,
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>",
+  "vocab_size": 62366
+}