Feature Extraction
Transformers
Safetensors
llama_bidirec
mergekit
Merge
custom_code
text-embeddings-inference
8-bit precision
Instructions to use KwangHwi/quantization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KwangHwi/quantization with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="KwangHwi/quantization", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("KwangHwi/quantization", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| base_model: [] | |
| library_name: transformers | |
| tags: | |
| - mergekit | |
| - merge | |
| # output_merge_multiSFT_v6_1 | |
| This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). | |
| ## Merge Details | |
| ### Merge Method | |
| This model was merged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method. | |
| ### Models Merged | |
| The following models were included in the merge: | |
| * /workspace/storage-shared/cnm/embedding/code/llama_1B_legal_retrieval_v2_2.3M_clean_150726_finetune2_07150726/checkpoint-8000 | |
| * /workspace/storage-shared/cnm/huyhq21_v2/mergekit/output_slerp_1B_data_v2_clean_v6 | |
| ### Configuration | |
| The following YAML configuration was used to produce this model: | |
| ```yaml | |
| # slices: | |
| # - sources: | |
| # - model: psmathur/orca_mini_v3_13b | |
| # layer_range: [0, 40] | |
| # - model: garage-bAInd/Platypus2-13B | |
| # layer_range: [0, 40] | |
| # # or, the equivalent models: syntax: | |
| # # models: | |
| # # - model: psmathur/orca_mini_v3_13b | |
| # # - model: garage-bAInd/Platypus2-13B | |
| # merge_method: slerp | |
| # base_model: psmathur/orca_mini_v3_13b | |
| # 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 # fallback for rest of tensors | |
| # dtype: float16 | |
| # slices: | |
| models: | |
| - model: /workspace/storage-shared/cnm/embedding/code/llama_1B_legal_retrieval_v2_2.3M_clean_150726_finetune2_07150726/checkpoint-8000 # Mô hình A (Base gốc) | |
| # layer_range: [0, 36] | |
| - model: /workspace/storage-shared/cnm/huyhq21_v2/mergekit/output_slerp_1B_data_v2_clean_v6 # Mô hình B (Đã Finetune) | |
| # layer_range: [0, 36] | |
| merge_method: slerp | |
| base_model: /workspace/storage-shared/cnm/huyhq21_v2/mergekit/output_slerp_1B_data_v2_clean_v6 # Mô hình B (Đã Finetune) | |
| parameters: | |
| t: | |
| #- filter: self_attn | |
| - filter: layers | |
| value: [0.2, 0.3, 0.5, 0.6, 0.7, 0.8] | |
| #- filter: mlp | |
| # value: [0.2, 0.3, 0.5, 0.6, 0.7, 0.8] | |
| - value: 0.5 # fallback cho layernorm | |
| dtype: bfloat16 | |
| # models: | |
| # - model: /workspace/storage-shared/cnm/cuongnq23/project_embeding/models/Qwen/Qwen3-Embedding-8B | |
| # - model: /workspace/storage-shared/cnm/embedding/code/qwen8b_SFT_kalm_vnlaw_3M/checkpoint-20000 | |
| # merge_method: slerp | |
| # base_model: /workspace/storage-shared/cnm/cuongnq23/project_embeding/models/Qwen/Qwen3-Embedding-8B | |
| # parameters: | |
| # t: | |
| # # Trượt dốc từ 10% (đáy) lên 90% (đỉnh) | |
| # - value: [0.1, 0.3, 0.5, 0.7, 0.9] | |
| # dtype: bfloat16 | |
| ``` | |