YAML Metadata
Warning:
empty or missing yaml metadata in repo card
(https://huggingface.co/docs/hub/model-cards#model-card-metadata)
Quantization made by Richard Erkhov.
Mistral-10.7B-v0.2 - GGUF
- Model creator: https://huggingface.co/Joseph717171/
- Original model: https://huggingface.co/Joseph717171/Mistral-10.7B-v0.2/
Name | Quant method | Size |
---|---|---|
Mistral-10.7B-v0.2.Q2_K.gguf | Q2_K | 3.73GB |
Mistral-10.7B-v0.2.IQ3_XS.gguf | IQ3_XS | 4.14GB |
Mistral-10.7B-v0.2.IQ3_S.gguf | IQ3_S | 4.37GB |
Mistral-10.7B-v0.2.Q3_K_S.gguf | Q3_K_S | 4.34GB |
Mistral-10.7B-v0.2.IQ3_M.gguf | IQ3_M | 4.51GB |
Mistral-10.7B-v0.2.Q3_K.gguf | Q3_K | 4.84GB |
Mistral-10.7B-v0.2.Q3_K_M.gguf | Q3_K_M | 4.84GB |
Mistral-10.7B-v0.2.Q3_K_L.gguf | Q3_K_L | 5.26GB |
Mistral-10.7B-v0.2.IQ4_XS.gguf | IQ4_XS | 5.43GB |
Mistral-10.7B-v0.2.Q4_0.gguf | Q4_0 | 5.66GB |
Mistral-10.7B-v0.2.IQ4_NL.gguf | IQ4_NL | 5.72GB |
Mistral-10.7B-v0.2.Q4_K_S.gguf | Q4_K_S | 5.7GB |
Mistral-10.7B-v0.2.Q4_K.gguf | Q4_K | 6.02GB |
Mistral-10.7B-v0.2.Q4_K_M.gguf | Q4_K_M | 6.02GB |
Mistral-10.7B-v0.2.Q4_1.gguf | Q4_1 | 6.27GB |
Mistral-10.7B-v0.2.Q5_0.gguf | Q5_0 | 6.89GB |
Mistral-10.7B-v0.2.Q5_K_S.gguf | Q5_K_S | 6.89GB |
Mistral-10.7B-v0.2.Q5_K.gguf | Q5_K | 7.08GB |
Mistral-10.7B-v0.2.Q5_K_M.gguf | Q5_K_M | 7.08GB |
Mistral-10.7B-v0.2.Q5_1.gguf | Q5_1 | 7.51GB |
Mistral-10.7B-v0.2.Q6_K.gguf | Q6_K | 8.2GB |
Mistral-10.7B-v0.2.Q8_0.gguf | Q8_0 | 10.62GB |
Original model description:
base_model: [] library_name: transformers tags: - mergekit - merge license: apache-2.0
Credit for the model card's description goes to ddh0 and mergekit
Looking for Mistral-10.7B-Instruct-v0.2?
Credit for access and conversion of Mistral-7B-v0.2 goes to alpindale (from MistalAI's weights to HF Transformers)
Mistral-10.7B-v0.2
This is Mistral-10.7B-v0.2, a depth-upscaled version of alpindale/Mistral-7B-v0.2-hf.
This model is intended to be used as a basis for further fine-tuning, or as a drop-in upgrade from the original 7 billion parameter model.
Paper detailing how Depth-Up Scaling works: SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the passthrough merge method.
Models Merged
The following models were included in the merge:
- /Users/jsarnecki/opt/Workspace/alpindale/Mistral-7B-v0.2-hf
Configuration
The following YAML configuration was used to produce this model:
dtype: bfloat16
merge_method: passthrough
slices:
- sources:
- layer_range: [0, 24]
model: /Users/jsarnecki/opt/Workspace/alpindale/Mistral-7B-v0.2-hf
- sources:
- layer_range: [8, 32]
model: /Users/jsarnecki/opt/Workspace/alpindale/Mistral-7B-v0.2-hf
- Downloads last month
- 0