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--- |
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license: apache-2.0 |
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tags: |
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- mergekit |
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- EmbeddedLLM/Mistral-7B-Merge-14-v0.1 |
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- OpenPipe/mistral-ft-optimized-1218 |
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- NLP |
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--- |
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# RLM-mini |
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RLM-mini is a 7.2 Billion parameter model,RLM-mini is designed to provide a robust and versatile natural language processing (NLP) capability, leveraging the strengths of two foundational models. By combining models from different sources, RLM-mini aims to inherit diverse linguistic features and training data nuances, resulting in improved performance across a wide range of NLP tasks. This includes more robust understanding and generation capabilities, especially in handling nuanced and context-heavy queries. The fine-tuning process integrates the best practices and optimizations from both parent models. This ensures that RLM-mini not only maintains high accuracy but also delivers responses more efficiently. |
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It is base model and requires Fine tuning. |
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### Two Merged Models |
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* [EmbeddedLLM/Mistral-7B-Merge-14-v0.1](https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.1) |
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* [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218) |
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# Usage |
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### Direct Model |
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``` python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("rudrashah/RLM-mini") |
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model = AutoModelForCausalLM.from_pretrained("rudrashah/RLM-mini") |
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input_token = tokenizer("How to make Pav Bhaji?", return_tensors="pt") |
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output = model.generate(**input_token, max_length=250) |
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output = tokenizer.decode(output[0]) |
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``` |
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### Using Pipeline |
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``` python |
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from transformers import AutoTokenizer |
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import transformers |
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import torch |
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model = "rudrashah/RLM-mini" |
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messages = [{"role": "user", "content": "What is a large language model?"}] |
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tokenizer = AutoTokenizer.from_pretrained(model) |
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prompt = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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pipeline = transformers.pipeline( |
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"text-generation", |
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model=model, |
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torch_dtype=torch.float16, |
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device_map="auto", |
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) |
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outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) |
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``` |