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RichardErkhov/EmbeddedLLM_-_Mistral-7B-Merge-02-v0-gguf
EmbeddedLLM_-_Mistral-7B-Merge-02-v0-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Downloads · 30 days
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.gguf95.2 GB · 100%
From the Hugging Face model README
Quantization made by Richard Erkhov.
Mistral-7B-Merge-02-v0 - GGUF
| Name | Quant method | Size |
|---|---|---|
| Mistral-7B-Merge-02-v0.Q2_K.gguf | Q2_K | 2.53GB |
| Mistral-7B-Merge-02-v0.IQ3_XS.gguf | IQ3_XS | 2.81GB |
| Mistral-7B-Merge-02-v0.IQ3_S.gguf | IQ3_S | 2.96GB |
| Mistral-7B-Merge-02-v0.Q3_K_S.gguf | Q3_K_S | 2.95GB |
| Mistral-7B-Merge-02-v0.IQ3_M.gguf | IQ3_M | 3.06GB |
| Mistral-7B-Merge-02-v0.Q3_K.gguf | Q3_K | 3.28GB |
| Mistral-7B-Merge-02-v0.Q3_K_M.gguf | Q3_K_M | 3.28GB |
| Mistral-7B-Merge-02-v0.Q3_K_L.gguf | Q3_K_L | 3.56GB |
| Mistral-7B-Merge-02-v0.IQ4_XS.gguf | IQ4_XS | 3.67GB |
| Mistral-7B-Merge-02-v0.Q4_0.gguf | Q4_0 | 3.83GB |
| Mistral-7B-Merge-02-v0.IQ4_NL.gguf | IQ4_NL | 3.87GB |
| Mistral-7B-Merge-02-v0.Q4_K_S.gguf | Q4_K_S | 3.86GB |
| Mistral-7B-Merge-02-v0.Q4_K.gguf | Q4_K | 4.07GB |
| Mistral-7B-Merge-02-v0.Q4_K_M.gguf | Q4_K_M | 4.07GB |
| Mistral-7B-Merge-02-v0.Q4_1.gguf | Q4_1 | 4.24GB |
| Mistral-7B-Merge-02-v0.Q5_0.gguf | Q5_0 | 4.65GB |
| Mistral-7B-Merge-02-v0.Q5_K_S.gguf | Q5_K_S | 4.65GB |
| Mistral-7B-Merge-02-v0.Q5_K.gguf | Q5_K | 4.78GB |
| Mistral-7B-Merge-02-v0.Q5_K_M.gguf | Q5_K_M | 4.78GB |
| Mistral-7B-Merge-02-v0.Q5_1.gguf | Q5_1 | 5.07GB |
| Mistral-7B-Merge-02-v0.Q6_K.gguf | Q6_K | 5.53GB |
| Mistral-7B-Merge-02-v0.Q8_0.gguf | Q8_0 | 7.17GB |
license: apache-2.0 language:
This is an experiment to compare merging 2 models using DARE TIES versus SLERP 🦙
We are mainly interested to compare against Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp
The 2 models involved in the merge as follows:
The yaml config file for the merge is:
models:
- model: mistralai/Mistral-7B-v0.1
# no parameters necessary for base model
- model: teknium/OpenHermes-2.5-Mistral-7B
parameters:
weight: 0.5
density: 0.5
- model: Intel/neural-chat-7b-v3-3
parameters:
weight: 0.5
density: 0.5
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
int8_mask: true
dtype: bfloat16
Note that with more tuning DARE TIES might achieve better results.
| DARE TIES | SLERP | |
|---|---|---|
| Average | 70.69 | 71.38 |
| ARC | 67.49 | 68.09 |
| HellaSwag | 85.78 | 86.2 |
| MMLU | 64.1 | 64.26 |
| TruthfulQA | 60.52 | 62.78 |
| Winogrande | 79.01 | 79.16 |
| GSM8K | 67.25 | 67.78 |