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add mixtral-8x7b

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README.md ADDED
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+ # Mixtral-8x7b-Instruct-v0.1-int4-ov
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+ * Model creator: [Mistral AI](https://huggingface.co/mistralai)
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+ * Original model: [Mixtral 8X7B Instruct v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)
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+
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+ ## Description
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+ This is [Mixtral-8x7b-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) model converted to [OpenVINO](https://docs.openvino.ai/2024/home.html) Intermediate Representation (IR) format with INT4 compressed weights using [NNCF](https://github.com/openvinotoolkit/nncf).
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+
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+ ## Compatibility
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+
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+ This provided IR is compatible with openvino starting with 2024.0.0 version and optimum-intel 1.16.0
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+
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+ ## Usage
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+
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+ ### Install required packages
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+
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+ To install the required components for using [Optimum Intel integration](https://huggingface.co/docs/optimum/intel/index) with the OpenVINO backend, do:
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+ ```
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+ pip install optimum[openvino]
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+ ```
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+
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+ ### Run model inference
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+ ```
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+ from transformers import AutoTokenizer
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+ from optimum.intel.openvino import OVModelForCausalLM
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+
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+ model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
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+ tokenizer = AutoTokenizer.from_pretrained(model_id)
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+ model = OVModelForCausalLM.from_pretrained(model_id)
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+
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+
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+ messages = [
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+ {"role": "user", "content": "What is your favourite condiment?"},
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+ {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
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+ {"role": "user", "content": "Do you have mayonnaise recipes?"}
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+ ]
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+
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+ inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
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+
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+ outputs = model.generate(inputs, max_new_tokens=20)
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+ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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+ ```
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+
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+ For more examples and possible optimizations please refer [OpenVINO Large Language Model Inference Guide](https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide.html)
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+
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+ ### Limitations
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+
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+ Please check original model card for model usage [limitations](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1#limitations)
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+
config.json ADDED
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+ {
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+ "_name_or_path": "/nfs/ov-share-05/data/cv_bench_cache/DL_benchmarking_models/mixtral-8x7b-v0.1/pytorch",
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+ "architectures": [
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+ "MixtralForCausalLM"
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+ ],
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+ "attention_dropout": 0.0,
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+ "eos_token_id": 2,
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+ "hidden_act": "silu",
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+ "hidden_size": 4096,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 14336,
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+ "max_position_embeddings": 32768,
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+ "model_type": "mixtral",
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+ "num_attention_heads": 32,
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+ "num_experts_per_tok": 2,
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+ "num_hidden_layers": 32,
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+ "num_key_value_heads": 8,
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+ "output_router_logits": false,
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+ "torch_dtype": "bfloat16",
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+ "transformers_version": "4.38.2",
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+ "use_cache": true,
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+ "vocab_size": 32000
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+ }
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