Create README.md

README.md

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+---
+license: openrail
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+
+## Original model card 
+
+Buy me a coffee if you like this project ;)
+<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
+
+#### Description 
+
+GGML Format model files for [This project](https://huggingface.co/James-WYang/BigTranslate).
+
+
+### inference 
+
+
+```python
+
+import ctransformers
+
+from ctransformers import AutoModelForCausalLM
+
+model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file,
+gpu_layers=32, model_type="llama")
+
+manual_input: str = "Tell me about your last dream, please."
+
+
+llm(manual_input, 
+      max_new_tokens=256, 
+      temperature=0.9, 
+      top_p= 0.7)
+
+```
+
+
+
+# Original model card
+## Model Details
+
+Vicuna is a chat assistant trained by fine-tuning Llama 2 on user-shared conversations collected from ShareGPT.
+
+- **Developed by:** [LMSYS](https://lmsys.org/)
+- **Model type:** An auto-regressive language model based on the transformer architecture
+- **License:** Llama 2 Community License Agreement	
+- **Finetuned from model:** [Llama 2](https://arxiv.org/abs/2307.09288)
+
+### Model Sources
+
+- **Repository:** https://github.com/lm-sys/FastChat
+- **Blog:** https://lmsys.org/blog/2023-03-30-vicuna/
+- **Paper:** https://arxiv.org/abs/2306.05685
+- **Demo:** https://chat.lmsys.org/
+
+## Uses
+
+The primary use of Vicuna is research on large language models and chatbots.
+The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.
+
+## How to Get Started with the Model
+
+- Command line interface: https://github.com/lm-sys/FastChat#vicuna-weights
+- APIs (OpenAI API, Huggingface API): https://github.com/lm-sys/FastChat/tree/main#api  
+
+## Training Details
+
+Vicuna v1.5 (16k) is fine-tuned from Llama 2 with supervised instruction fine-tuning and linear RoPE scaling.
+The training data is around 125K conversations collected from ShareGPT.com. These conversations are packed into sequences that contain 16K tokens each.
+See more details in the "Training Details of Vicuna Models" section in the appendix of this [paper](https://arxiv.org/pdf/2306.05685.pdf).
+
+## Evaluation
+
+Vicuna is evaluated with standard benchmarks, human preference, and LLM-as-a-judge. See more details in this [paper](https://arxiv.org/pdf/2306.05685.pdf) and [leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard).
+
+## Difference between different versions of Vicuna
+
+See [vicuna_weights_version.md](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md)