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--- |
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base_model: |
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- Kukedlc/NeuralSirKrishna-7b |
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- Kukedlc/NeuralArjuna-7B-DT |
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- Kukedlc/NeuralMaths-Experiment-7b |
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- Kukedlc/NeuralSynthesis-7B-v0.1 |
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library_name: transformers |
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tags: |
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- mergekit |
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- merge |
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license: apache-2.0 |
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--- |
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# NeuralStockFusion-7b |
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# merge |
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This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). |
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## Merge Details |
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### Merge Method |
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This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [Kukedlc/NeuralSirKrishna-7b](https://huggingface.co/Kukedlc/NeuralSirKrishna-7b) as a base. |
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### Models Merged |
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The following models were included in the merge: |
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* [Kukedlc/NeuralArjuna-7B-DT](https://huggingface.co/Kukedlc/NeuralArjuna-7B-DT) |
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* [Kukedlc/NeuralMaths-Experiment-7b](https://huggingface.co/Kukedlc/NeuralMaths-Experiment-7b) |
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* [Kukedlc/NeuralSynthesis-7B-v0.1](https://huggingface.co/Kukedlc/NeuralSynthesis-7B-v0.1) |
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### Configuration |
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The following YAML configuration was used to produce this model: |
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```yaml |
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models: |
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- model: Kukedlc/NeuralMaths-Experiment-7b |
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- model: Kukedlc/NeuralArjuna-7B-DT |
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- model: Kukedlc/NeuralSirKrishna-7b |
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- model: Kukedlc/NeuralSynthesis-7B-v0.1 |
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merge_method: model_stock |
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base_model: Kukedlc/NeuralSirKrishna-7b |
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dtype: bfloat16 |
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``` |
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# Model Inference: |
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``` python |
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!pip install -qU transformers accelerate bitsandbytes |
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer, BitsAndBytesConfig |
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import torch |
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bnb_config = BitsAndBytesConfig( |
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load_in_4bit=True, |
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bnb_4bit_use_double_quant=True, |
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bnb_4bit_quant_type="nf4", |
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bnb_4bit_compute_dtype=torch.bfloat16 |
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) |
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MODEL_NAME = 'Kukedlc/NeuralStockFusion-7b' |
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) |
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model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map='cuda:0', quantization_config=bnb_config) |
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inputs = tokenizer(["[INST] What is a large language model, in spanish \n[/INST]\n"], return_tensors="pt").to('cuda') |
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streamer = TextStreamer(tokenizer) |
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# Despite returning the usual output, the streamer will also print the generated text to stdout. |
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_ = model.generate(**inputs, streamer=streamer, max_new_tokens=256, do_sample=True, temperature=0.7, repetition_penalty=1.4, top_p=0.9) |
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``` |
