Hugging Face's logo

Locutusque
/
LocutusqueXFelladrin-TinyMistral248M-Instruct

Text Generation
Transformers
PyTorch
English
mistral
Merge
text-generation-inference
Inference Endpoints
Model card Files Community
2
LocutusqueXFelladrin-TinyMistral248M-Instruct / README.md
Locutusque's picture
Locutusque
Update README.md
11130c4
preview code
|
raw
history blame
2.36 kB
metadata
license: apache-2.0
datasets:
  - Locutusque/inst_mix_v2_top_100k
language:
  - en
pipeline_tag: text-generation
widget:
  - 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|> 
inference:
  parameters:
    temperature: 0.4
    do_sample: true
    top_p: 0.34
    top_k: 30
    max_new_tokens: 250
    repetition_penalty: 1.15

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.

Evaluation

Coming soon...