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Model: afrideva/no-prompt-1.3b-GGUF
Source: Original Platform

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README.md

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+---
+base_model: appvoid/no-prompt-1.3b
+datasets:
+- appvoid/no-prompt-15k
+inference: false
+language:
+- en
+license: apache-2.0
+model_creator: appvoid
+model_name: no-prompt-1.3b
+pipeline_tag: text-generation
+quantized_by: afrideva
+tags:
+- gguf
+- ggml
+- quantized
+- q2_k
+- q3_k_m
+- q4_k_m
+- q5_k_m
+- q6_k
+- q8_0
+---
+# appvoid/no-prompt-1.3b-GGUF
+
+Quantized GGUF model files for [no-prompt-1.3b](https://huggingface.co/appvoid/no-prompt-1.3b) from [appvoid](https://huggingface.co/appvoid)
+
+
+| Name | Quant method | Size |
+| ---- | ---- | ---- |
+| [no-prompt-1.3b.fp16.gguf](https://huggingface.co/afrideva/no-prompt-1.3b-GGUF/resolve/main/no-prompt-1.3b.fp16.gguf) | fp16 | 2.69 GB  |
+| [no-prompt-1.3b.q2_k.gguf](https://huggingface.co/afrideva/no-prompt-1.3b-GGUF/resolve/main/no-prompt-1.3b.q2_k.gguf) | q2_k | 631.52 MB  |
+| [no-prompt-1.3b.q3_k_m.gguf](https://huggingface.co/afrideva/no-prompt-1.3b-GGUF/resolve/main/no-prompt-1.3b.q3_k_m.gguf) | q3_k_m | 704.72 MB  |
+| [no-prompt-1.3b.q4_k_m.gguf](https://huggingface.co/afrideva/no-prompt-1.3b-GGUF/resolve/main/no-prompt-1.3b.q4_k_m.gguf) | q4_k_m | 873.27 MB  |
+| [no-prompt-1.3b.q5_k_m.gguf](https://huggingface.co/afrideva/no-prompt-1.3b-GGUF/resolve/main/no-prompt-1.3b.q5_k_m.gguf) | q5_k_m | 1.00 GB  |
+| [no-prompt-1.3b.q6_k.gguf](https://huggingface.co/afrideva/no-prompt-1.3b-GGUF/resolve/main/no-prompt-1.3b.q6_k.gguf) | q6_k | 1.17 GB  |
+| [no-prompt-1.3b.q8_0.gguf](https://huggingface.co/afrideva/no-prompt-1.3b-GGUF/resolve/main/no-prompt-1.3b.q8_0.gguf) | q8_0 | 1.43 GB  |
+
+
+
+## Original Model Card:
+![palmer](https://huggingface.co/appvoid/no-prompt-1.3b/resolve/main/_ccd1a5dd-2ddc-4d5a-8163-fd6d1b39f5f4.jpeg?download=true)
+# no-prompt
+### a sheared-llama-1.3b fine-tuning
+This model uses an 1.3 billion parameters model as base to be further fine-tuned on the same data as palmer. It works pretty good and even surpasses sota model on `hellaswag`.
+
+### evaluation
+|Model|	ARC_C|	HellaSwag|	PIQA|	Winogrande|
+|------|-----|-----------|------|-------------|
+|tinyllama-2t| 0.2807| 0.5463| 0.7067| 0.5683|
+|palmer-001  | 0.2807| 0.5524| 0.7106| 0.5896|
+|sheared-1.3b| 0.2910| 0.5935| 0.7339| 0.5809|
+|no-prompt-1.3b| 0.3157| **0.6022**| 0.7334| 0.5864|
+|falcon-rw-1b-instruct-openorca (sota) | **0.3362**|   0.5997|  **0.7394**|  **0.6148**|
+
+This model was trained on less than 25% of the dataset yet achieves competitive performance to current sota on open llm leaderboard.
+
+### training
+Training took ~5 P100 gpu hours. It was trained on 15,000 gpt-4 shuffled samples. no-prompt was fine-tuned using lower learning rates ensuring it keeps as much general knowledge as possible.
+
+### prompt
+```
+no prompt
+```
+
+### limitations
+Hallucinations are frequent, just as any transformer model this size.
+
+<a href="https://ko-fi.com/appvoid" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 48px !important;width: 180px !important; filter: invert(70%);" ></a>

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