metadata
license: apache-2.0
tags:
- code
- mistral
Mistral-7B-codealpaca
We are thrilled to introduce the Mistral-7B-codealpaca model. This variant is optimized and demonstrates potential in assisting developers as a coding companion. We welcome contributions from testers and enthusiasts to help evaluate its performance.
Training Details
The model was trained using 3xRTX 3090 for 118 hours.
Quantised Model Links:
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)
Well, the results are better than I expected
- Base:
{'pass@1': 0.47560975609756095} - Base + Extra:
{'pass@1': 0.4329268292682927}
For reference, we've provided the performance of the original Mistral model alongside 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:
The following are the configurations for the 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: