Mistral-7B-codealpaca
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.
Training Details
I trained the model using 3xRTX 3090 for 118 hours.
Quantised Model Links:
- https://huggingface.co/TheBloke/Mistral-7B-codealpaca-lora-GPTQ
- https://huggingface.co/TheBloke/Mistral-7B-codealpaca-lora-GGUF
- https://huggingface.co/TheBloke/Mistral-7B-codealpaca-lora-AWQ
Download by qBittorrent:
Torrent file: https://github.com/Nondzu/LlamaTor/blob/torrents/torrents/Nondzu_Mistral-7B-codealpaca-lora.torrent
Dataset:
- Dataset Name: theblackcat102/evol-codealpaca-v1
- Dataset Link: theblackcat102/evol-codealpaca-v1
Prompt template: Alpaca
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
Performance (evalplus)
Human eval plus: https://github.com/evalplus/evalplus
Well, the results are better than I expected:
- Base:
{'pass@1': 0.47560975609756095}
- Base + Extra:
{'pass@1': 0.4329268292682927}
For reference, I've provided the performance of the original Mistral model alongside my Mistral-7B-code-16k-qlora model.
** Nondzu/Mistral-7B-code-16k-qlora**:
- Base:
{'pass@1': 0.3353658536585366}
- Base + Extra:
{'pass@1': 0.2804878048780488}
** mistralai/Mistral-7B-Instruct-v0.1**:
- Base:
{'pass@1': 0.2926829268292683}
- Base + Extra:
{'pass@1': 0.24390243902439024}
Model Configuration:
Here are the configurations for my Mistral-7B-codealpaca-lora:
base_model: mistralai/Mistral-7B-Instruct-v0.1
base_model_config: mistralai/Mistral-7B-Instruct-v0.1
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
is_mistral_derived_model: true
load_in_8bit: true
load_in_4bit: false
strict: false
datasets:
- path: theblackcat102/evol-codealpaca-v1
type: oasst
dataset_prepared_path:
val_set_size: 0.01
output_dir: ./nondzu/Mistral-7B-codealpaca-test14
adapter: lora
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
Additional Projects:
For other related projects, you can check out:
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