Question Answering
Transformers
Safetensors
mistral
text-generation
Merge
mergekit
lazymergekit
huggingface/CodeBERTa-language-id
Sharathhebbar24/code_gpt2
text-generation-inference
Instructions to use nagayama0706/coding_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nagayama0706/coding_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="nagayama0706/coding_model")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("nagayama0706/coding_model") model = AutoModelForMultimodalLM.from_pretrained("nagayama0706/coding_model") - Notebooks
- Google Colab
- Kaggle
File size: 411 Bytes
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slices:
- sources:
- model: OpenPipe/mistral-ft-optimized-1218
layer_range: [0, 32]
- model: mlabonne/NeuralHermes-2.5-Mistral-7B
layer_range: [0, 32]
merge_method: slerp
base_model: OpenPipe/mistral-ft-optimized-1218
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
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