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Model: yam-peleg/Hebrew-Mistral-7B
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+language:
+- en
+- he
+library_name: transformers
+---
+# Hebrew-Mistral-7B
+
+Hebrew-Mistral-7B is an open-source Large Language Model (LLM) pretrained in hebrew and english pretrained with 7B billion parameters, based on Mistral-7B-v1.0 from Mistral.
+
+It has an extended hebrew tokenizer with 64,000 tokens and is continuesly pretrained from Mistral-7B on tokens in both English and Hebrew.
+
+The resulting model is a powerful general-purpose language model suitable for a wide range of natural language processing tasks, with a focus on Hebrew language understanding and generation.
+
+### Usage
+
+Below are some code snippets on how to get quickly started with running the model.
+
+First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase.
+
+### Running on CPU
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("yam-peleg/Hebrew-Mistral-7B")
+model = AutoModelForCausalLM.from_pretrained("yam-peleg/Hebrew-Mistral-7B")
+
+input_text = "שלום! מה שלומך היום?"
+input_ids = tokenizer(input_text, return_tensors="pt")
+
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0]))
+```
+
+### Running on GPU
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("yam-peleg/Hebrew-Mistral-7B")
+model = AutoModelForCausalLM.from_pretrained("yam-peleg/Hebrew-Mistral-7B", device_map="auto")
+
+input_text = "שלום! מה שלומך היום?"
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0]))
+```
+
+### Running with 4-Bit precision
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+
+tokenizer = AutoTokenizer.from_pretrained("yam-peleg/Hebrew-Mistral-7B")
+model = AutoModelForCausalLM.from_pretrained("yam-peleg/Hebrew-Mistral-7B", quantization_config = BitsAndBytesConfig(load_in_4bit=True))
+
+input_text = "שלום! מה שלומך היום?"
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0])
+```
+
+### Notice
+
+Hebrew-Mistral-7B is a pretrained base model and therefore does not have any moderation mechanisms.
+
+### Authors
+- Trained by Yam Peleg.
+- In collaboration with Jonathan Rouach and Arjeo, inc.
+

added_tokens.json

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+{
+  "[PAD]": 64000
+}

config.json

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+{
+  "_name_or_path": "/home/yam/mistral_hebrew_full_session_6/checkpoint-7035/",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
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+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.38.1",
+  "use_cache": false,
+  "vocab_size": 64001
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.38.1"
+}

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