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README.md ADDED
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+ ---
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+ license: cc-by-4.0
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+ ---
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+
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+ # saiga-phi-3-mini-4k
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+
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+ saiga-phi-3-mini-4k is an SFT fine-tuned version of microsoft/Phi-3-mini-4k-instruct using a custom training dataset.
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+ This model was made with [Phinetune]()
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+
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+ ## Process
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+ - Learning Rate: 1.41e-05
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+ - Maximum Sequence Length: 2048
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+ - Dataset: IlyaGusev/ru_turbo_saiga
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+ - Split: train
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+
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+ ## 💻 Usage
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+ ```python
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+ !pip install -qU transformers
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+ from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
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+
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+ model = "Slavator096/saiga-phi-3-mini-4k"
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+ tokenizer = AutoTokenizer.from_pretrained(model)
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+
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+ # Example prompt
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+ prompt = "Your example prompt here"
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+
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+ # Generate a response
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+ model = AutoModelForCausalLM.from_pretrained(model)
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+ pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
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+ outputs = pipeline(prompt, max_length=50, num_return_sequences=1)
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+ print(outputs[0]["generated_text"])
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+ ```
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+ }
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+ {
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+ "_name_or_path": "microsoft/Phi-3-mini-4k-instruct",
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+ "architectures": [
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+ "Phi3ForCausalLM"
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+ ],
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+ "attention_dropout": 0.0,
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+ "auto_map": {
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+ "AutoConfig": "microsoft/Phi-3-mini-4k-instruct--configuration_phi3.Phi3Config",
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+ "AutoModelForCausalLM": "microsoft/Phi-3-mini-4k-instruct--modeling_phi3.Phi3ForCausalLM"
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+ },
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+ "bos_token_id": 1,
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+ "embd_pdrop": 0.0,
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+ "eos_token_id": 32000,
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+ "hidden_act": "silu",
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+ "hidden_size": 3072,
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+ "initializer_range": 0.02,
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+ "max_position_embeddings": 4096,
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+ "model_type": "phi3",
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+ "tie_word_embeddings": false,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.40.0",
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+ }
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