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KnutJaegersberg_-_deacon-13b-gguf
RichardErkhov/KnutJaegersberg_-_deacon-13b-gguf
KnutJaegersberg_-_deacon-13b-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
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147 GB
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.gguf171 GB · 100%
At a glance
- Access
- Public
- Created
- May 19, 2024
- Updated
- May 20, 2024
- SHA
- cea06044
Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
deacon-13b - GGUF
- Model creator: https://huggingface.co/KnutJaegersberg/
- Original model: https://huggingface.co/KnutJaegersberg/deacon-13b/
| Name | Quant method | Size |
|---|---|---|
| deacon-13b.Q2_K.gguf | Q2_K | 4.52GB |
| deacon-13b.IQ3_XS.gguf | IQ3_XS | 4.99GB |
| deacon-13b.IQ3_S.gguf | IQ3_S | 5.27GB |
| deacon-13b.Q3_K_S.gguf | Q3_K_S | 5.27GB |
| deacon-13b.IQ3_M.gguf | IQ3_M | 5.57GB |
| deacon-13b.Q3_K.gguf | Q3_K | 5.9GB |
| deacon-13b.Q3_K_M.gguf | Q3_K_M | 5.9GB |
| deacon-13b.Q3_K_L.gguf | Q3_K_L | 6.45GB |
| deacon-13b.IQ4_XS.gguf | IQ4_XS | 6.54GB |
| deacon-13b.Q4_0.gguf | Q4_0 | 6.86GB |
| deacon-13b.IQ4_NL.gguf | IQ4_NL | 6.9GB |
| deacon-13b.Q4_K_S.gguf | Q4_K_S | 6.91GB |
| deacon-13b.Q4_K.gguf | Q4_K | 7.33GB |
| deacon-13b.Q4_K_M.gguf | Q4_K_M | 7.33GB |
| deacon-13b.Q4_1.gguf | Q4_1 | 7.61GB |
| deacon-13b.Q5_0.gguf | Q5_0 | 8.36GB |
| deacon-13b.Q5_K_S.gguf | Q5_K_S | 8.36GB |
| deacon-13b.Q5_K.gguf | Q5_K | 8.6GB |
| deacon-13b.Q5_K_M.gguf | Q5_K_M | 8.6GB |
| deacon-13b.Q5_1.gguf | Q5_1 | 9.1GB |
| deacon-13b.Q6_K.gguf | Q6_K | 9.95GB |
| deacon-13b.Q8_0.gguf | Q8_0 | 12.88GB |
Original model description:
license: cc-by-nc-4.0 datasets:
- KnutJaegersberg/facehugger
This model was fine tuned on AI filtered subsets of GPT-4 based subset of the Dolphin dataset and EvolInstruct V2. It has not been explicitly aligned to positive, negative or bureaucratically prescribed value systems. It might kill us all! Time to shit your pants, regulators. I literally put black goo on Dolphin-7B sperm, which then fertilized Evolved Instructions... What's different is evil... ;) I intend to train 3 sizes.
Prompt Example:
### System:
You are an AI assistant. User will give you a task. Your goal is to complete the task as faithfully as you can. While performing the task think step-by-step and justify your steps.
### Instruction:
How do you fine tune a large language model?
### Response:
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 46.78 |
| ARC (25-shot) | 57.85 |
| HellaSwag (10-shot) | 82.63 |
| MMLU (5-shot) | 55.25 |
| TruthfulQA (0-shot) | 39.33 |
| Winogrande (5-shot) | 76.32 |
| GSM8K (5-shot) | 10.39 |
| DROP (3-shot) | 5.67 |
- Created
- May 19, 2024
- Updated
- May 20, 2024