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@@ -5,7 +5,17 @@ license: apache-2.0
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  model_creator: Intel
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  model_name: Neural Chat 7B v3-1
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  model_type: mistral
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- prompt_template: '{prompt}
 
 
 
 
 
 
 
 
 
 
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  '
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  quantized_by: TheBloke
@@ -69,11 +79,17 @@ Here is an incomplete list of clients and libraries that are known to support GG
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  <!-- repositories-available end -->
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  <!-- prompt-template start -->
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- ## Prompt template: Unknown
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  ```
 
 
 
 
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  {prompt}
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  ```
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  <!-- prompt-template end -->
@@ -191,7 +207,7 @@ Windows Command Line users: You can set the environment variable by running `set
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  Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
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  ```shell
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- ./main -ngl 32 -m neural-chat-7b-v3-1.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"
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  ```
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  Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
@@ -285,9 +301,9 @@ And thank you again to a16z for their generous grant.
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  # Original model card: Intel's Neural Chat 7B v3-1
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- ## Finetuning on [habana](https://habana.ai/) HPU
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- This model is a fine-tuned model based on [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the open source dataset [Open-Orca/SlimOrca](https://huggingface.co/datasets/Open-Orca/SlimOrca). Then we align it with DPO algorithm. For more details, you can refer our blog: [NeuralChat: Simplifying Supervised Instruction Fine-Tuning and Reinforcement Aligning](https://medium.com/intel-analytics-software/neuralchat-simplifying-supervised-instruction-fine-tuning-and-reinforcement-aligning-for-chatbots-d034bca44f69) and [The Practice of Supervised Fine-tuning and Direct Preference Optimization on Habana Gaudi2](https://medium.com/@NeuralCompressor/the-practice-of-supervised-finetuning-and-direct-preference-optimization-on-habana-gaudi2-a1197d8a3cd3).
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  ## Model date
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  Neural-chat-7b-v3-1 was trained between September and October, 2023.
@@ -317,10 +333,22 @@ The following hyperparameters were used during training:
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  - total_train_batch_size: 64
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  - total_eval_batch_size: 8
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- - lr_scheduler_type: linear
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- - lr_scheduler_warmup_ratio: 0.02
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  - num_epochs: 2.0
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  ## Inference with transformers
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  ```shell
@@ -346,6 +374,5 @@ The NeuralChat team with members from Intel/SATG/AIA/AIPT. Core team members: Ka
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  ## Useful links
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  * Intel Neural Compressor [link](https://github.com/intel/neural-compressor)
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  * Intel Extension for Transformers [link](https://github.com/intel/intel-extension-for-transformers)
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- * Intel Extension for PyTorch [link](https://github.com/intel/intel-extension-for-pytorch)
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  <!-- original-model-card end -->
 
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  model_creator: Intel
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  model_name: Neural Chat 7B v3-1
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  model_type: mistral
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+ prompt_template: '### System:
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+
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+ {system_message}
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+
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+
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+ ### User:
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+
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+ {prompt}
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+
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+
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+ ### Assistant:
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  '
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  quantized_by: TheBloke
 
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  <!-- repositories-available end -->
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  <!-- prompt-template start -->
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+ ## Prompt template: Orca-Hashes
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  ```
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+ ### System:
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+ {system_message}
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+
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+ ### User:
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  {prompt}
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+ ### Assistant:
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+
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  ```
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  <!-- prompt-template end -->
 
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  Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
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  ```shell
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+ ./main -ngl 32 -m neural-chat-7b-v3-1.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### System:\n{system_message}\n\n### User:\n{prompt}\n\n### Assistant:"
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  ```
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  Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
 
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  # Original model card: Intel's Neural Chat 7B v3-1
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+ ## Fine-tuning on [Habana](https://habana.ai/) Gaudi2
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+ This model is a fine-tuned model based on [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the open source dataset [Open-Orca/SlimOrca](https://huggingface.co/datasets/Open-Orca/SlimOrca). Then we align it with DPO algorithm. For more details, you can refer our blog: [The Practice of Supervised Fine-tuning and Direct Preference Optimization on Habana Gaudi2](https://medium.com/@NeuralCompressor/the-practice-of-supervised-finetuning-and-direct-preference-optimization-on-habana-gaudi2-a1197d8a3cd3).
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  ## Model date
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  Neural-chat-7b-v3-1 was trained between September and October, 2023.
 
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  - total_train_batch_size: 64
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  - total_eval_batch_size: 8
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: cosine
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+ - lr_scheduler_warmup_ratio: 0.03
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  - num_epochs: 2.0
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+ ## Prompt Template
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+
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+ ```
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+ ### System:
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+ {system}
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+ ### User:
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+ {usr}
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+ ### Assistant:
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+
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+ ```
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+
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+
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  ## Inference with transformers
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  ```shell
 
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  ## Useful links
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  * Intel Neural Compressor [link](https://github.com/intel/neural-compressor)
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  * Intel Extension for Transformers [link](https://github.com/intel/intel-extension-for-transformers)
 
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  <!-- original-model-card end -->