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Model: Norod78/SmolLM-135M-FakyPedia-EngHeb
Source: Original Platform

.gitattributes

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+SmolLM-135M-FakyPedia-EngHeb-BF16.gguf filter=lfs diff=lfs merge=lfs -text

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|>\%
+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
+```

SmolLM-135M-FakyPedia-EngHeb-BF16.gguf

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config.json

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+{
+  "_name_or_path": "Norod78/SmolLM-135M-FakyPedia-EngHeb",
+  "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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model_config.json

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+{
+  "name": "Config for Chat ID 1723546179511",
+  "load_params": {
+    "n_ctx": 512,
+    "n_batch": 512,
+    "rope_freq_base": 0,
+    "rope_freq_scale": 0,
+    "n_gpu_layers": 20,
+    "use_mlock": true,
+    "main_gpu": 0,
+    "tensor_split": [
+      0
+    ],
+    "seed": -1,
+    "f16_kv": true,
+    "use_mmap": true,
+    "no_kv_offload": false,
+    "num_experts_used": 0
+  },
+  "inference_params": {
+    "n_threads": 4,
+    "n_predict": -1,
+    "top_k": 30,
+    "min_p": 0,
+    "top_p": 1,
+    "temp": 0.5,
+    "repeat_penalty": 1.2,
+    "input_prefix": "<|endoftext|>\\\\%",
+    "input_suffix": "",
+    "antiprompt": [],
+    "pre_prompt": "Below is an instruction that describes a task. Write a response that appropriately completes the request.",
+    "pre_prompt_suffix": "",
+    "pre_prompt_prefix": "",
+    "seed": -1,
+    "tfs_z": 1,
+    "typical_p": 1,
+    "repeat_last_n": 64,
+    "frequency_penalty": 0,
+    "presence_penalty": 0,
+    "n_keep": 0,
+    "logit_bias": {},
+    "mirostat": 0,
+    "mirostat_tau": 5,
+    "mirostat_eta": 0.1,
+    "memory_f16": true,
+    "multiline_input": false,
+    "penalize_nl": true
+  }
+}

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": {
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+      "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
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+    "8": {
+      "content": "<issue_start>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "content": "<issue_comment>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    "10": {
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+      "lstrip": false,
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+      "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 %}{{'<|endoftext|>\\% + message['role'] + '\n' + message['content'] + '<|endoftext|>' + '\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
+}