5a752e9079
Model: afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF Source: Original Platform
76 lines
4.4 KiB
Markdown
76 lines
4.4 KiB
Markdown
---
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base_model: Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct
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datasets:
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- Locutusque/inst_mix_v2_top_100k
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inference: false
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language:
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- en
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license: apache-2.0
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model_creator: Locutusque
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model_name: LocutusqueXFelladrin-TinyMistral248M-Instruct
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pipeline_tag: text-generation
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quantized_by: afrideva
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tags:
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- gguf
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- ggml
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- quantized
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- q2_k
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- q3_k_m
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- q4_k_m
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- q5_k_m
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- q6_k
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- q8_0
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widget:
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- text: '<|USER|> Design a Neo4j database and Cypher function snippet to Display Extreme
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Dental hygiene: Using Mouthwash for Analysis for Beginners. Implement if/else
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or switch/case statements to handle different conditions related to the Consent.
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Provide detailed comments explaining your control flow and the reasoning behind
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each decision. <|ASSISTANT|> '
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- text: '<|USER|> Write me a story about a magical place. <|ASSISTANT|> '
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- text: '<|USER|> Write me an essay about the life of George Washington <|ASSISTANT|> '
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- text: '<|USER|> Solve the following equation 2x + 10 = 20 <|ASSISTANT|> '
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- text: '<|USER|> Craft me a list of some nice places to visit around the world. <|ASSISTANT|> '
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- text: '<|USER|> How to manage a lazy employee: Address the employee verbally. Don''t
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allow an employee''s laziness or lack of enthusiasm to become a recurring issue.
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Tell the employee you''re hoping to speak with them about workplace expectations
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and performance, and schedule a time to sit down together. Question: To manage
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a lazy employee, it is suggested to talk to the employee. True, False, or Neither?
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<|ASSISTANT|> '
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---
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# Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF
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Quantized GGUF model files for [LocutusqueXFelladrin-TinyMistral248M-Instruct](https://huggingface.co/Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct) from [Locutusque](https://huggingface.co/Locutusque)
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| Name | Quant method | Size |
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| ---- | ---- | ---- |
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| [locutusquexfelladrin-tinymistral248m-instruct.fp16.gguf](https://huggingface.co/afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF/resolve/main/locutusquexfelladrin-tinymistral248m-instruct.fp16.gguf) | fp16 | 497.76 MB |
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| [locutusquexfelladrin-tinymistral248m-instruct.q2_k.gguf](https://huggingface.co/afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF/resolve/main/locutusquexfelladrin-tinymistral248m-instruct.q2_k.gguf) | q2_k | 116.20 MB |
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| [locutusquexfelladrin-tinymistral248m-instruct.q3_k_m.gguf](https://huggingface.co/afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF/resolve/main/locutusquexfelladrin-tinymistral248m-instruct.q3_k_m.gguf) | q3_k_m | 131.01 MB |
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| [locutusquexfelladrin-tinymistral248m-instruct.q4_k_m.gguf](https://huggingface.co/afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF/resolve/main/locutusquexfelladrin-tinymistral248m-instruct.q4_k_m.gguf) | q4_k_m | 156.61 MB |
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| [locutusquexfelladrin-tinymistral248m-instruct.q5_k_m.gguf](https://huggingface.co/afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF/resolve/main/locutusquexfelladrin-tinymistral248m-instruct.q5_k_m.gguf) | q5_k_m | 180.17 MB |
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| [locutusquexfelladrin-tinymistral248m-instruct.q6_k.gguf](https://huggingface.co/afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF/resolve/main/locutusquexfelladrin-tinymistral248m-instruct.q6_k.gguf) | q6_k | 205.20 MB |
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| [locutusquexfelladrin-tinymistral248m-instruct.q8_0.gguf](https://huggingface.co/afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF/resolve/main/locutusquexfelladrin-tinymistral248m-instruct.q8_0.gguf) | q8_0 | 265.26 MB |
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## Original Model Card:
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# LocutusqueXFelladrin-TinyMistral248M-Instruct
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This model was created by merging Locutusque/TinyMistral-248M-Instruct and Felladrin/TinyMistral-248M-SFT-v4 using mergekit. After the two models were merged, the resulting model was further trained on ~20,000 examples on the Locutusque/inst_mix_v2_top_100k at a low learning rate to further normalize weights. The following is the YAML config used to merge:
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```yaml
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models:
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- model: Felladrin/TinyMistral-248M-SFT-v4
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parameters:
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weight: 0.5
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- model: Locutusque/TinyMistral-248M-Instruct
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parameters:
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weight: 1.0
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merge_method: linear
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dtype: float16
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```
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The resulting model combines the best of both worlds. With Locutusque/TinyMistral-248M-Instruct's coding capabilities and reasoning skills, and Felladrin/TinyMistral-248M-SFT-v4's low hallucination and instruction-following capabilities. The resulting model has an incredible performance considering its size.
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## Evaluation
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Coming soon... |