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RichardErkhov/
NeuralNovel_-_Gecko-7B-v0.1-gguf

Name: NeuralNovel_-_Gecko-7B-v0.1-gguf
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Gecko-7B-v0.1 - GGUF

Model creator: https://huggingface.co/NeuralNovel/
Original model: https://huggingface.co/NeuralNovel/Gecko-7B-v0.1/

Name	Quant method	Size
Gecko-7B-v0.1.Q2_K.gguf	Q2_K	2.53GB
Gecko-7B-v0.1.IQ3_XS.gguf	IQ3_XS	2.81GB
Gecko-7B-v0.1.IQ3_S.gguf	IQ3_S	2.96GB
Gecko-7B-v0.1.Q3_K_S.gguf	Q3_K_S	2.95GB
Gecko-7B-v0.1.IQ3_M.gguf	IQ3_M	3.06GB
Gecko-7B-v0.1.Q3_K.gguf	Q3_K	3.28GB
Gecko-7B-v0.1.Q3_K_M.gguf	Q3_K_M	3.28GB
Gecko-7B-v0.1.Q3_K_L.gguf	Q3_K_L	3.56GB
Gecko-7B-v0.1.IQ4_XS.gguf	IQ4_XS	3.67GB
Gecko-7B-v0.1.Q4_0.gguf	Q4_0	3.83GB
Gecko-7B-v0.1.IQ4_NL.gguf	IQ4_NL	3.87GB
Gecko-7B-v0.1.Q4_K_S.gguf	Q4_K_S	3.86GB
Gecko-7B-v0.1.Q4_K.gguf	Q4_K	4.07GB
Gecko-7B-v0.1.Q4_K_M.gguf	Q4_K_M	4.07GB
Gecko-7B-v0.1.Q4_1.gguf	Q4_1	4.24GB
Gecko-7B-v0.1.Q5_0.gguf	Q5_0	4.65GB
Gecko-7B-v0.1.Q5_K_S.gguf	Q5_K_S	4.65GB
Gecko-7B-v0.1.Q5_K.gguf	Q5_K	4.78GB
Gecko-7B-v0.1.Q5_K_M.gguf	Q5_K_M	4.78GB
Gecko-7B-v0.1.Q5_1.gguf	Q5_1	5.07GB
Gecko-7B-v0.1.Q6_K.gguf	Q6_K	5.53GB
Gecko-7B-v0.1.Q8_0.gguf	Q8_0	7.17GB

Original model description:

license: apache-2.0 library_name: transformers base_model: mistralai/Mistral-7B-Instruct-v0.2 inference: false model-index:

name: Gecko-7B-v0.1 results:
- task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics:
  - type: acc_norm value: 61.35 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=NeuralNovel/Gecko-7B-v0.1 name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics:
  - type: acc_norm value: 83.36 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=NeuralNovel/Gecko-7B-v0.1 name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics:
  - type: acc value: 61.05 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=NeuralNovel/Gecko-7B-v0.1 name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics:
  - type: mc2 value: 62.6 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=NeuralNovel/Gecko-7B-v0.1 name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics:
  - type: acc value: 77.58 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=NeuralNovel/Gecko-7B-v0.1 name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics:
  - type: acc value: 41.55 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=NeuralNovel/Gecko-7B-v0.1 name: Open LLM Leaderboard

Gecko-7B-v0.1

Designed to generate instructive and narrative text, with a focus on mathematics & numeracy.

Full-parameter fine-tune (FFT) of Mistral-7B-Instruct-v0.2, with apache-2.0 license.

You may download and use this model for research, training and commercial purposes.

This model is suitable for commercial deployment.

Data-set

The model was finetuned using the Neural-Mini-Math dataset (Currently Private)

Summary

Fine-tuned with the intention of following all prompt directions, making it more suitable for roleplay and problem solving.

Out-of-Scope Use

The model may not perform well in scenarios unrelated to instructive and narrative text generation. Misuse or applications outside its designed scope may result in suboptimal outcomes.

Bias, Risks, and Limitations

This model may not work as intended. As such all users are encouraged to use this model with caution and respect.

This model is for testing and research purposes only, it has reduced levels of alignment and as a result may produce NSFW or harmful content. The user is responsible for their output and must use this model responsibly.

Hardware and Training


  n_epochs = 3,
  n_checkpoints = 3,
  batch_size = 12,
  learning_rate = 1e-5,

Sincere appreciation to Techmind for their generous sponsorship.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	64.58
AI2 Reasoning Challenge (25-Shot)	61.35
HellaSwag (10-Shot)	83.36
MMLU (5-Shot)	61.05
TruthfulQA (0-shot)	62.60
Winogrande (5-shot)	77.58
GSM8k (5-shot)	41.55

RichardErkhov/NeuralNovel_-_Gecko-7B-v0.1-gguf