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sethuiyer/SynthIQ-7b
SynthIQ-7b is a text generation model from sethuiyer. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as llama2.
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Downloads · 30 days
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.safetensors14.5 GB · 100%
From the Hugging Face model README
This is SynthIQ, rated 92.23/100 by GPT-4 across varied complex prompts. I used mergekit to merge models.
| Benchmark Name | Score |
|---|---|
| ARC | 65.87 |
| HellaSwag | 85.82 |
| MMLU | 64.75 |
| TruthfulQA | 57.00 |
| Winogrande | 78.69 |
| GSM8K | 64.06 |
| AGIEval | 42.67 |
| GPT4All | 73.71 |
| Bigbench | 44.59 |
Tested to work well with autogen and CrewAI
GGUF Files
Q4_K_M - medium, balanced quality - recommended
Q_6_K - very large, extremely low quality loss
Q8_0 - very large, extremely low quality loss - not recommended
Important Update: SynthIQ is now available on Ollama. You can use it by running the command ollama run stuehieyr/synthiq in your
terminal. If you have limited computing resources, check out this video to learn how to run it on
a Google Colab backend.
slices:
- sources:
- model: Weyaxi/OpenHermes-2.5-neural-chat-v3-3-openchat-3.5-1210-Slerp
layer_range: [0, 32]
- model: uukuguy/speechless-mistral-six-in-one-7b
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-v0.1
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5 # fallback for rest of tensors
tokenizer_source: union
dtype: bfloat16
<!-- prompt-template start -->
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
<!-- prompt-template end -->
License is LLama2 license as uukuguy/speechless-mistral-six-in-one-7b is llama2 license.
Detailed results can be found here
Detailed results can be found here
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 69.37 |
| AI2 Reasoning Challenge (25-Shot) | 65.87 |
| HellaSwag (10-Shot) | 85.82 |
| MMLU (5-Shot) | 64.75 |
| TruthfulQA (0-shot) | 57.00 |
| Winogrande (5-shot) | 78.69 |
| GSM8k (5-shot) | 64.06 |