102 lines
3.0 KiB
Markdown
102 lines
3.0 KiB
Markdown
---
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license: apache-2.0
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tags:
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- code
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- mistral
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---
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# Mistral-7B-codealpaca
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I am thrilled to introduce my Mistral-7B-codealpaca model. This variant is optimized and demonstrates potential in assisting developers as a coding companion. I welcome contributions from testers and enthusiasts to help evaluate its performance.
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## Training Details
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I trained the model using 3xRTX 3090 for 118 hours.
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[](https://github.com/OpenAccess-AI-Collective/axolotl)
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## Quantised Model Links:
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1. https://huggingface.co/TheBloke/Mistral-7B-codealpaca-lora-GPTQ
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2. https://huggingface.co/TheBloke/Mistral-7B-codealpaca-lora-GGUF
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3. https://huggingface.co/TheBloke/Mistral-7B-codealpaca-lora-AWQ
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## Download by qBittorrent:
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#### Torrent file: https://github.com/Nondzu/LlamaTor/blob/torrents/torrents/Nondzu_Mistral-7B-codealpaca-lora.torrent
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## Dataset:
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- Dataset Name: theblackcat102/evol-codealpaca-v1
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- Dataset Link: [theblackcat102/evol-codealpaca-v1](https://huggingface.co/datasets/theblackcat102/evol-codealpaca-v1)
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## Prompt template: Alpaca
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```
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Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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{prompt}
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### Response:
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```
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## Performance (evalplus)
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Human eval plus: https://github.com/evalplus/evalplus
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Well, the results are better than I expected:
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- Base: `{'pass@1': 0.47560975609756095}`
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- Base + Extra: `{'pass@1': 0.4329268292682927}`
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For reference, I've provided the performance of the original Mistral model alongside my Mistral-7B-code-16k-qlora model.
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** [Nondzu/Mistral-7B-code-16k-qlora](https://huggingface.co/Nondzu/Mistral-7B-code-16k-qlora)**:
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- Base: `{'pass@1': 0.3353658536585366}`
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- Base + Extra: `{'pass@1': 0.2804878048780488}`
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** [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)**:
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- Base: `{'pass@1': 0.2926829268292683}`
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- Base + Extra: `{'pass@1': 0.24390243902439024}`
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## Model Configuration:
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Here are the configurations for my Mistral-7B-codealpaca-lora:
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```yaml
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base_model: mistralai/Mistral-7B-Instruct-v0.1
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base_model_config: mistralai/Mistral-7B-Instruct-v0.1
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model_type: MistralForCausalLM
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tokenizer_type: LlamaTokenizer
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is_mistral_derived_model: true
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load_in_8bit: true
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load_in_4bit: false
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strict: false
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datasets:
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- path: theblackcat102/evol-codealpaca-v1
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type: oasst
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dataset_prepared_path:
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val_set_size: 0.01
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output_dir: ./nondzu/Mistral-7B-codealpaca-test14
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adapter: lora
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sequence_len: 4096
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sample_packing: true
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pad_to_sequence_len: true
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lora_r: 32
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lora_alpha: 16
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lora_dropout: 0.05
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lora_target_modules:
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lora_target_linear: true
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```
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## Additional Projects:
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For other related projects, you can check out:
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- [LlamaTor on GitHub](https://github.com/Nondzu/LlamaTor) |