base_model, datasets, inference, language, license, model_creator, model_name, pipeline_tag, quantized_by, tags, widget

base_model	datasets	inference	language	license	model_creator	model_name	pipeline_tag	quantized_by	tags	widget
Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct	Locutusque/inst_mix_v2_top_100k	false	en	apache-2.0	Locutusque	LocutusqueXFelladrin-TinyMistral248M-Instruct	text-generation	afrideva	gguf ggml quantized q2_k q3_k_m q4_k_m q5_k_m q6_k q8_0	text <|USER|> Design a Neo4j database and Cypher function snippet to Display Extreme Dental hygiene: Using Mouthwash for Analysis for Beginners. Implement if/else or switch/case statements to handle different conditions related to the Consent. Provide detailed comments explaining your control flow and the reasoning behind each decision. <|ASSISTANT|> text <|USER|> Write me a story about a magical place. <|ASSISTANT|> text <|USER|> Write me an essay about the life of George Washington <|ASSISTANT|> text <|USER|> Solve the following equation 2x + 10 = 20 <|ASSISTANT|> text <|USER|> Craft me a list of some nice places to visit around the world. <|ASSISTANT|> text <|USER|> How to manage a lazy employee: Address the employee verbally. Don't allow an employee's laziness or lack of enthusiasm to become a recurring issue. Tell the employee you're hoping to speak with them about workplace expectations and performance, and schedule a time to sit down together. Question: To manage a lazy employee, it is suggested to talk to the employee. True, False, or Neither? <|ASSISTANT|>

Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF

Quantized GGUF model files for LocutusqueXFelladrin-TinyMistral248M-Instruct from Locutusque

Name	Quant method	Size
locutusquexfelladrin-tinymistral248m-instruct.fp16.gguf	fp16	497.76 MB
locutusquexfelladrin-tinymistral248m-instruct.q2_k.gguf	q2_k	116.20 MB
locutusquexfelladrin-tinymistral248m-instruct.q3_k_m.gguf	q3_k_m	131.01 MB
locutusquexfelladrin-tinymistral248m-instruct.q4_k_m.gguf	q4_k_m	156.61 MB
locutusquexfelladrin-tinymistral248m-instruct.q5_k_m.gguf	q5_k_m	180.17 MB
locutusquexfelladrin-tinymistral248m-instruct.q6_k.gguf	q6_k	205.20 MB
locutusquexfelladrin-tinymistral248m-instruct.q8_0.gguf	q8_0	265.26 MB

Original Model Card:

LocutusqueXFelladrin-TinyMistral248M-Instruct

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:

models:
  - model: Felladrin/TinyMistral-248M-SFT-v4
    parameters:
      weight: 0.5
  - model: Locutusque/TinyMistral-248M-Instruct
    parameters:
      weight: 1.0
merge_method: linear
dtype: float16

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.

Evaluation

Coming soon...