初始化项目，由ModelHub XC社区提供模型

Model: remyxai/ffmperative-7b
Source: Original Platform

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README.md

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+---
+license: llama2
+---
+
+# Model Card for FFMPerative-7B
+
+## Model Details
+
+This is a Llama 2 7B Large Language Model (LLM), fine-tuned specifically to automate video production workflows. 
+It is designed to interact with FFMPerative, a tool that leverages machine learning and the FFmpeg software suite to perform a variety of 
+video editing tasks using natural language input. 
+
+
+### Model Description
+
+
+- **Developed by:** [remyx.ai]
+- **Model type:** [LlaMA2-7B]
+- **License:** [Meta]
+- **Finetuned from model [optional]:** [LlaMA2]
+
+## Uses
+
+The main use case for this model is to assist in video editing tasks. 
+Users can leverage it to execute commands in natural language to FFMPerative for tasks such as cropping, resizing, rotating videos, 
+making gifs, adjusting audio levels, and many more. The model can be particularly useful for people without technical skills, 
+helping them interact with complex video editing tasks in a simplified, user-friendly manner.
+
+This checkpoint was fine-tuned on a subset of `HuggingFaceH4/CodeAlpaca_20K` augmented with 500 instances of FFMPerative Tool composition for
+practical video editing workflows.
+
+The training instances are based on various video editing tasks and their corresponding commands in FFMPerative, with example questions and 
+answers demonstrating the interaction between a user and the video editing tool. 
+Please refer to the GitHub repository readme for more examples of the training data used.
+
+## Bias, Risks, and Limitations
+
+Please note that this model is designed for English language inputs and may not perform well with inputs in other languages.
+Although this model can interpret and execute a wide range of commands, it might sometimes struggle with ambiguous instructions, 
+complex sequences of commands, or instructions for tasks that are not included in its training data.
+
+Please ensure you double-check the output of the model for critical tasks, and remember that it won't replace professional 
+video editors for more advanced video editing workflows.
+
+## How to Get Started with the Model
+
+Use the code below to get started. You can instantiate a local agent and pass additional tools:
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, LocalAgent, load_tool
+
+model = AutoModelForCausalLM.from_pretrained("remyxai/ffmperative",
+                                              device_map="auto",
+                                              torch_dtype=torch.bfloat16,
+                                              rope_scaling={"type": "dynamic", "factor": 2.0},
+                                              load_in_8bit=True)
+tokenizer = AutoTokenizer.from_pretrained("remyxai/ffmperative")
+
+# More tools in our spaces: https://huggingface.co/remyxai
+tools = [load_tool("remyxai/video-compression-tool"), load_tool("remyxai/video-frame-sample-tool")] 
+
+
+agent = LocalAgent(model, tokenizer, additional_tools=tools)
+agent.run("Compress my video '/path/to/vid.mp4' and save it to '/path/to/compressed_vid.mp4'")
+```
+
+
+## Training Details
+
+### Training Data
+
+Training data is a combination of [HuggingFaceH4/CodeAlpaca_20K](https://huggingface.co/datasets/HuggingFaceH4/CodeAlpaca_20K) and our custom generated data reflecting the tools available in ffmperative - [remyxai/ffmperative](https://huggingface.co/datasets/remyxai/ffmperative)
+
+
+### Training Procedure 
+
+Using Parameter Efficient Fine-Tuning (PEFT), according to this [guide](https://huggingface.co/blog/llama2#fine-tuning-with-peft), we fine-tuned
+LlaMA2 with this [script](https://github.com/lvwerra/trl/blob/main/examples/scripts/sft_trainer.py).
+
+## Evaluation
+
+We evaluated the model performance by measuring its ability to accurately interpret and execute video editing commands. 
+Due to the proprietary nature of the evaluation process, specific metrics are not available.
+
+The model generally performs well, but please report any inconsistencies or errors you encounter when using the model. 
+We appreciate your feedback and will use it to improve the model further.
+
+
+### Model Architecture and Objective
+
+Meta's LlaMA2-7B
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+
+

config.json

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+{
+  "_name_or_path": "meta-llama/Llama-2-7b-hf",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
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+  "eos_token_id": 2,
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+  "max_position_embeddings": 4096,
+  "model_type": "llama",
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+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
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+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.32.0.dev0",
+  "use_cache": true,
+  "vocab_size": 32000
+}

configuration.json

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generation_config.json

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+  "pad_token_id": 0,
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+  "top_p": 0.6,
+  "transformers_version": "4.32.0.dev0"
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special_tokens_map.json

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