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RichardErkhov/
M4-ai_-_NeuralReyna-Mini-1.8B-v0.2-gguf

Name: M4-ai_-_NeuralReyna-Mini-1.8B-v0.2-gguf
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NeuralReyna-Mini-1.8B-v0.2 - GGUF

Model creator: https://huggingface.co/M4-ai/
Original model: https://huggingface.co/M4-ai/NeuralReyna-Mini-1.8B-v0.2/

Name	Quant method	Size
NeuralReyna-Mini-1.8B-v0.2.Q2_K.gguf	Q2_K	0.79GB
NeuralReyna-Mini-1.8B-v0.2.Q3_K_S.gguf	Q3_K_S	0.89GB
NeuralReyna-Mini-1.8B-v0.2.Q3_K.gguf	Q3_K	0.95GB
NeuralReyna-Mini-1.8B-v0.2.Q3_K_M.gguf	Q3_K_M	0.95GB
NeuralReyna-Mini-1.8B-v0.2.Q3_K_L.gguf	Q3_K_L	0.98GB
NeuralReyna-Mini-1.8B-v0.2.IQ4_XS.gguf	IQ4_XS	1.01GB
NeuralReyna-Mini-1.8B-v0.2.Q4_0.gguf	Q4_0	1.04GB
NeuralReyna-Mini-1.8B-v0.2.IQ4_NL.gguf	IQ4_NL	1.05GB
NeuralReyna-Mini-1.8B-v0.2.Q4_K_S.gguf	Q4_K_S	1.08GB
NeuralReyna-Mini-1.8B-v0.2.Q4_K.gguf	Q4_K	1.13GB
NeuralReyna-Mini-1.8B-v0.2.Q4_K_M.gguf	Q4_K_M	1.13GB
NeuralReyna-Mini-1.8B-v0.2.Q4_1.gguf	Q4_1	1.13GB
NeuralReyna-Mini-1.8B-v0.2.Q5_0.gguf	Q5_0	1.22GB
NeuralReyna-Mini-1.8B-v0.2.Q5_K_S.gguf	Q5_K_S	1.24GB
NeuralReyna-Mini-1.8B-v0.2.Q5_K.gguf	Q5_K	1.28GB
NeuralReyna-Mini-1.8B-v0.2.Q5_K_M.gguf	Q5_K_M	1.28GB
NeuralReyna-Mini-1.8B-v0.2.Q5_1.gguf	Q5_1	1.31GB
NeuralReyna-Mini-1.8B-v0.2.Q6_K.gguf	Q6_K	1.47GB
NeuralReyna-Mini-1.8B-v0.2.Q8_0.gguf	Q8_0	1.82GB

Original model description:

language:

en license: apache-2.0 tags:
conversational datasets:
Intel/orca_dpo_pairs
Locutusque/Hercules-v3.0 inference: parameters: do_sample: true temperature: 0.8 top_p: 0.95 top_k: 40 min_new_tokens: 2 max_new_tokens: 250 repetition_penalty: 1.1 widget:
text: Hello who are you? example_title: Identity
text: What can you do? example_title: Capabilities
text: Create a fastapi endpoint to retrieve the weather given a zip code. example_title: Coding model-index:
name: NeuralReyna-Mini-1.8B-v0.2 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: 37.8 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=M4-ai/NeuralReyna-Mini-1.8B-v0.2 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: 60.51 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=M4-ai/NeuralReyna-Mini-1.8B-v0.2 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: 45.04 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=M4-ai/NeuralReyna-Mini-1.8B-v0.2 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: 37.75 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=M4-ai/NeuralReyna-Mini-1.8B-v0.2 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: 60.93 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=M4-ai/NeuralReyna-Mini-1.8B-v0.2 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: 27.07 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=M4-ai/NeuralReyna-Mini-1.8B-v0.2 name: Open LLM Leaderboard

NeuralReyna-Mini-1.8B-v0.2

Description

Taken aloobun/Reyna-Mini-1.8B-v0.2 and further fine-tuned it using DPO using the Intel/orca_dpo_pairs dataset.

This model has capabilities in coding, math, science, roleplay, and function calling.

This model was trained on OpenAI's ChatML prompt format.

Evaluation

AGIEval:

GPT4ALL:

Tasks	Version	Filter	Metric	Value		Stderr
arc_challenge	1	none	acc	0.3208	±	0.0136
		none	acc_norm	0.3336	±	0.0138
arc_easy	1	none	acc	0.6035	±	0.0100
		none	acc_norm	0.5833	±	0.0101
boolq	2	none	acc	0.6526	±	0.0083
hellaswag	1	none	acc	0.4556	±	0.0050
		none	acc_norm	0.6076	±	0.0049
openbookqa	1	none	acc	0.2600	±	0.0196
		none	acc_norm	0.3460	±	0.0213
piqa	1	none	acc	0.7236	±	0.0104
		none	acc_norm	0.7307	±	0.0104
winogrande	1	none	acc	0.6062	±	0.0137

Disclaimer

This model may have overfitted to the DPO training data, and may not perform well.

Contributions

Thanks to @aloobun and @Locutusque for their contributions to this model.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	44.85
AI2 Reasoning Challenge (25-Shot)	37.80
HellaSwag (10-Shot)	60.51
MMLU (5-Shot)	45.04
TruthfulQA (0-shot)	37.75
Winogrande (5-shot)	60.93
GSM8k (5-shot)	27.07

RichardErkhov/M4-ai_-_NeuralReyna-Mini-1.8B-v0.2-gguf