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---
license: apache-2.0
tags:
- code
- mistral
---
# 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.
[](https://github.com/OpenAccess-AI-Collective/axolotl)
## Quantised Model Links:
1. https://huggingface.co./TheBloke/Mistral-7B-codealpaca-lora-GPTQ
2. https://huggingface.co./TheBloke/Mistral-7B-codealpaca-lora-GGUF
3. 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](https://huggingface.co./datasets/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](https://huggingface.co./Nondzu/Mistral-7B-code-16k-qlora)**:
- Base: `{'pass@1': 0.3353658536585366}`
- Base + Extra: `{'pass@1': 0.2804878048780488}`
** [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co./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:
```yaml
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:
- [LlamaTor on GitHub](https://github.com/Nondzu/LlamaTor) |