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README.md
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- unsloth
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- mistral
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- trl
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---
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# Uploaded model
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- **Developed by:** thesven
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- **License:** apache-2.0
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- **Finetuned from model :** unsloth/mistral-7b-v0.3-bnb-4bit
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This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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- unsloth
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- mistral
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- trl
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- code
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datasets:
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- thesven/AetherCode-v1
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---
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/6324ce4d5d0cf5c62c6e3c5a/NlTeemUNYet9p5963Sfhr.png)
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# Uploaded model
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- **Developed by:** thesven
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- **License:** apache-2.0
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- **Finetuned from model :** unsloth/mistral-7b-v0.3-bnb-4bit
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This model is an iteration of the Mistral 7B model, fine-tuned using Supervised Fine-Tuning (SFT) on the AetherCode-v1 dataset specifically for code-related tasks. It combines the advanced capabilities of the base Mistral 7B model with specialized training to enhance its performance in software development contexts.
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## Usage
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```python
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from unsloth import FastLanguageModel
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max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
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dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
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load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
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alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
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### Instruction:
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{}
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### Input:
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{}
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### Response:
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{}"""
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name = "thesven/Aether-Code-Mistral-7B-0.3-v1", # YOUR MODEL YOU USED FOR TRAINING
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max_seq_length = max_seq_length,
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dtype = dtype,
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load_in_4bit = load_in_4bit,
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)
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FastLanguageModel.for_inference(model) # Enable native 2x faster inference
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# alpaca_prompt = You MUST copy from above!
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inputs = tokenizer(
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[
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alpaca_prompt.format(
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"You are an expert python developer, help me with my questions.", # instruction
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"How can I use puppeteer to get a mobile screen shot of a website?", # input
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"", # output - leave this blank for generation!
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),
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], return_tensors = "pt").to("cuda")
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outputs = model.generate(**inputs, max_new_tokens = 4000, use_cache = True)
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print(tokenizer.batch_decode(outputs))
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```
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This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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