commit 60240832b192cc34d40075d162c5b898c56f56f3 Author: ModelHub XC Date: Sat May 30 07:19:12 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: SicariusSicariiStuff/Hebrew_Nemo Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..feec1cf --- /dev/null +++ b/.gitattributes @@ -0,0 +1,62 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bin.* filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zstandard filter=lfs diff=lfs merge=lfs -text +*.tfevents* filter=lfs diff=lfs merge=lfs -text +*.db* filter=lfs diff=lfs merge=lfs -text +*.ark* filter=lfs diff=lfs merge=lfs -text +**/*ckpt*data* filter=lfs diff=lfs merge=lfs -text +**/*ckpt*.meta filter=lfs diff=lfs merge=lfs -text +**/*ckpt*.index filter=lfs diff=lfs merge=lfs -text + +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.gguf* filter=lfs diff=lfs merge=lfs -text +*.ggml filter=lfs diff=lfs merge=lfs -text +*.llamafile* filter=lfs diff=lfs merge=lfs -text +*.pt2 filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text + +model-00005-of-00013.safetensors filter=lfs diff=lfs merge=lfs -text +model-00002-of-00013.safetensors filter=lfs diff=lfs merge=lfs -text +model-00008-of-00013.safetensors filter=lfs diff=lfs merge=lfs -text +model-00009-of-00013.safetensors filter=lfs diff=lfs merge=lfs -text +model-00003-of-00013.safetensors filter=lfs diff=lfs merge=lfs -text +model-00004-of-00013.safetensors filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text +model-00007-of-00013.safetensors filter=lfs diff=lfs merge=lfs -text +model-00006-of-00013.safetensors filter=lfs diff=lfs merge=lfs -text +model-00013-of-00013.safetensors filter=lfs diff=lfs merge=lfs -text +model-00010-of-00013.safetensors filter=lfs diff=lfs merge=lfs -text +model-00012-of-00013.safetensors filter=lfs diff=lfs merge=lfs -text +model-00011-of-00013.safetensors filter=lfs diff=lfs merge=lfs -text +model-00001-of-00013.safetensors filter=lfs diff=lfs merge=lfs -text \ No newline at end of file diff --git a/Images/Hebrew_Nemo.png b/Images/Hebrew_Nemo.png new file mode 100644 index 0000000..b4f2689 Binary files /dev/null and b/Images/Hebrew_Nemo.png differ diff --git a/README.md b/README.md new file mode 100644 index 0000000..6b64ef7 --- /dev/null +++ b/README.md @@ -0,0 +1,297 @@ +--- +language: + - he + - en +license: apache-2.0 +tags: + - mistral + - nemo + - hebrew + - llm + - text-generation + - instruction-tuned + - chat +pipeline_tag: text-generation +base_model: mistralai/Mistral-Nemo-Base-2407 +library_name: transformers +widget: + - text: "Hebrew_Nemo" + output: + url: https://huggingface.co/SicariusSicariiStuff/Hebrew_Nemo/resolve/main/Images/Hebrew_Nemo.png +--- + +# Hebrew_Nemo: State-of-the-Art Hebrew Language Model + +--- + +
+ Hebrew_Nemo + + +
+ + +
+ 12B + + +
+ + +--- + +
+ Developed by: SicariusSicariiStuff +
+ +--- + +**Hebrew_Nemo** is a state-of-the-art (SOTA) **Hebrew language large language model** specifically optimized for Hebrew language understanding and generation. Built upon the Mistral Nemo architecture, this model represents a significant advancement in Hebrew NLP capabilities, combining the robust multilingual foundations of Mistral Nemo with extensive Hebrew-specific fine-tuning and optimization. + +As part of [SicariusSicariiStuff](https://huggingface.co/SicariusSicariiStuff) efforts to truly democratize AI, [Hebrew_Nemo](https://huggingface.co/SicariusSicariiStuff/Hebrew_Nemo) is released with a permissive **Apache 2.0** license. The model demonstrates competitive performance with **Gemma3-27B**, one of the world’s leading open-source models in multilingual capabilities—despite Gemma3-27B being **more than twice its size**. This result highlights Hebrew_Nemo’s efficiency and effectiveness, making SOTA capabilities widely available for consumers, as well as corporations. + +Unfortunately, Gemma-3-27b-it doesn't benchmark well, but I still believe Gemma-3-27b-it is by far the best multi-lingual model: + +| Model | Average | SNLI Acc | QA (HeQ) | Translation BLEU | Israeli Trivia | Params (B) | +|-------|---------|----------|----------|------------------|----------------|------------| +| google/gemma-3-27b-pt | 69.5 | 85.24 | 78.27 | 36.45 | 70.43 | 27 | +| google/gemma-3-27b-it | 13.41 | 0 | 80.31 | 0.17 | 0 | 27 | + +--- + +# Benchmarks + +--- + +**Hebrew_Nemo** demonstrates SOTA performance for its size, with particularly **outstanding results in Hebrew translation**. At only **12B parameters**, it achieves a **BLEU score of 30.83**, outperforming significantly larger models such as DeepSeek-14B and AI21 Jamba-Mini (52B)— a model more than x4 times its size. + +The model maintains **high competence across reasoning and QA**, with **SNLI accuracy of 79.76** and **HeQ score of 70.51**, indicating solid sentence-level understanding and contextual reasoning in Hebrew. Its **Israeli Trivia score (50.83)** demonstrates exceptional knowledge for its size, coming very close to a model more than 4x times its size, while vastly outperforming models of similar and even of a slightly larger size. + + +| Model | Average | SNLI Acc | QA (HeQ) | Translation BLEU | Israeli Trivia | Params (B) | +| ---------------------------------------- | --------: | --------: | --------: | ---------------: | -------------: | ---------: | +| **Hebrew_Nemo** | **57.98** | 79.76 | 70.51 | **30.83** | 50.83 | 12 | +| ai21labs/AI21-Jamba-1.5-Mini | 54.68 | 69.52 | 69.38 | 22.00 | **57.81** | 52 | +| deepseek-ai/DeepSeek-R1-Distill-Qwen-14B | 53.19 | **85.48** | 71.38 | 22.99 | 32.89 | 14 | +| SicariusSicariiStuff/Zion_Alpha | 53.55 | 84.05 | 67.67 | 27.93 | 34.55 | 7 | +| Qwen/Qwen3-8B | 53.54 | 80.00 | **78.53** | 25.73 | 29.90 | 8 | +| Mistral-Nemo-Base-2407 | 51.24 | 65.95 | 68.48 | 28.99 | 41.53 | 12.0 | + +--- + +**Hebrew_Nemo** also **vastly improves** upon the original Mistral Nemo by adding massive amounts of new knowledge while refining existing capabilities: + +| Metric | Hebrew_Nemo | Mistral-Nemo-Base | (% Improvement) | +| :------------------- | ----------: | ----------------: | ----------------: | +| **Average** | **57.98** | 51.24 | **+13.2%** | +| **SNLI Accuracy** | **79.76** | 65.95 | **+20.9%** | +| **QA (HeQ)** | **70.51** | 68.48 | **+3.0%** | +| **Translation BLEU** | **30.83** | 28.99 | **+6.3%** | +| **Israeli Trivia** | **50.83** | 41.53 | **+22.4%** | + +---- + + + +### Technical Overview + +- **Model Type:** Causal Language Model (Decoder-only Transformer) +- **Base Architecture:** Mistral Nemo +- **Language Focus:** Hebrew (עברית) with maintained multilingual capabilities +- **License:** Apache 2.0 +- **Parameters:** 12B +- **Context Length:** 128K tokens +- **Layers:** 40 +- **Dim:** 5,120 +- **Head dim:** 128 +- **Hidden dim:** 14,336 +- **Activation Function:** SwiGLU +- **Number of heads:** 32 +- **Number of kv-heads:** 8 (GQA) +- **Vocabulary size:** 2**17 ~= 128k +- **Rotary embeddings (theta = 1M)** + +### Primary Use Cases + +- **Hebrew Text Generation:** High-quality content creation in modern Hebrew +- **Translation:** Bidirectional translation between Hebrew and other languages +- **Question Answering:** Advanced reasoning and comprehension in Hebrew contexts +- **Dialogue Systems:** Conversational AI applications for Hebrew speakers +- **Text Classification:** Sentiment analysis, topic modeling, and categorization of Hebrew content +- **Named Entity Recognition:** Extraction of entities from Hebrew text +- **Summarization:** Concise summaries of Hebrew documents and articles + +### Out-of-Scope Uses + +- Real-time critical decision-making systems (medical, legal, financial) without human oversight +- Generation of content intended to deceive or manipulate +- Applications requiring 100% factual accuracy without verification + + +## Training Data and Training Methodology + +Hebrew_Nemo was trained on a diverse corpus including: + +| Source Type | Description | Language Coverage | +|--------------|--------------|------------------| +| Hebrew Wikipedia | Encyclopedia-style text | 100% Hebrew | +| Hebrew Literature & Proverbs | Classic and modern | 100% Hebrew | +| Hebrew-English Code-Mix | Social media & dialogue | 70% Hebrew / 30% English | +| Synthetic Data | Instruction-following & reasoning | Mixed | + +Data was filtered, normalized, and token-balanced to reduce bias and improve generalization across dialects. + +Additional data trained: + +- Modern Hebrew web text and news articles +- Hebrew literature and academic publications +- Biblical and Rabbinic Hebrew texts for cultural depth +- Hebrew social media and conversational data +- Technical documentation in Hebrew +- Parallel corpora for translation capabilities + +--- + +**The training process involved:** + +1. Continued pre-training on Hebrew-rich datasets +2. Instruction fine-tuning on Hebrew task-specific data +3. Alignment through RLHF/DPO for Hebrew linguistic preferences + +--- + +## 🚀 Key Features + +- **Native Hebrew Understanding:** Trained on millions of high-quality Hebrew documents spanning literature, news, Wikipedia, academic, and colloquial domains. +- **Contextual Mastery:** Handles complex anaphora, idiomatic expressions, and mixed Hebrew-English text with high fidelity. +- **Instruction-Tuned:** Aligned for chat, Q&A, summarization, and reasoning use cases. +- **Cultural Awareness:** Sensitive to Hebrew cultural, religious, and social nuances. +- **Optimized Inference:** Enhanced performance with Mistral’s memory-efficient attention and dynamic context window. + +--- + +# Out of scope usage +* Generating disinformation or biased political content +* Automated decision-making without human oversight + +--- + +## ⚙️ Limitations + +* May reflect **training corpus biases** (e.g., urban dialect prevalence, widespread opinions in Israeli social media) +* Limited performance on **rare biblical or archaic Hebrew** +* Occasionally mixes Hebrew and English when the context is ambiguous +* Does not include alignment for safety moderation out of the box + +--- + +# Model instruction template: ChatML + +``` +<|im_start|>system +You answer the questions in Hebrew.<|im_end|> +<|im_start|>User +{prompt}<|im_end|> +<|im_start|>AI answer +``` + +--- + +## 🗣️ Example Usage + +### Basic Inference + +```python +from transformers import AutoModelForCausalLM, AutoTokenizer + +model_name = "SicariusSicariiStuff/Hebrew_Nemo" +tokenizer = AutoTokenizer.from_pretrained(model_name) +model = AutoModelForCausalLM.from_pretrained( + model_name, + torch_dtype="auto", + device_map="auto" +) + +prompt = "מהי בינה מלאכותית?" +inputs = tokenizer(prompt, return_tensors="pt").to(model.device) +outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7) +print(tokenizer.decode(outputs[0], skip_special_tokens=True)) +``` + +--- + +### Chat Format + +```python +messages = [ + {"role": "user", "content": "ספר לי על ההיסטוריה של ירושלים"} +] + +formatted_prompt = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True +) + +inputs = tokenizer(formatted_prompt, return_tensors="pt").to(model.device) +outputs = model.generate(**inputs, max_new_tokens=512) +print(tokenizer.decode(outputs[0], skip_special_tokens=True)) +``` + +### Quantization (for lower VRAM) + +```python +from transformers import BitsAndBytesConfig + +quantization_config = BitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_compute_dtype=torch.bfloat16 +) + +model = AutoModelForCausalLM.from_pretrained( + model_name, + quantization_config=quantization_config, + device_map="auto" +) +``` + +--- + +## Available quantizations: + +- Original: [FP16](https://huggingface.co/SicariusSicariiStuff/Hebrew_Nemo) +- GGUF: [Static Quants](https://huggingface.co/SicariusSicariiStuff/Hebrew_Nemo_GGUF) +- Specialized: [FP8](https://huggingface.co/SicariusSicariiStuff/Hebrew_Nemo_FP8) +- Mobile (ARM): [Q4_0](https://huggingface.co/SicariusSicariiStuff/Hebrew_Nemo_ARM) + +--- + + +## Citation + +```bibtex +@misc{hebrew_nemo_2025, + author = {SicariusSicariiStuff}, + title = {Hebrew_Nemo: State-of-the-Art Hebrew Language Model}, + year = {2025}, + publisher = {Hugging Face}, + url = {https://huggingface.co/SicariusSicariiStuff/Hebrew_Nemo} +} +``` + + +## 🧰 Acknowledgements + +* [Mistral](https://mistral.ai/) for the base architecture +* [NVIDIA NeMo](https://developer.nvidia.com/nemo) framework inspiration +* Employee#11 for her unwavering support + +## Contact + +For questions, issues, or collaboration opportunities: +- **HuggingFace:** [@SicariusSicariiStuff](https://huggingface.co/SicariusSicariiStuff) +- **Issues:** Report technical issues on the model repository + + +### Model Card Authors +- [@SicariusSicariiStuff](https://huggingface.co/SicariusSicariiStuff) \ No newline at end of file diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000..2116e45 --- /dev/null +++ b/chat_template.jinja @@ -0,0 +1,4 @@ +{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + ' +' + message['content'] + '<|im_end|>' + ' +'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant +' }}{% endif %} diff --git a/config.json b/config.json new file mode 100644 index 0000000..966d89f --- /dev/null +++ b/config.json @@ -0,0 +1,27 @@ +{ + "_name_or_path": "SicariusSicariiStuff/Hebrew_Nemo", + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 5120, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 131072, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 40, + "num_key_value_heads": 8, + "rms_norm_eps": 1e-05, + "rope_theta": 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