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README.md
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---
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base_model: vince62s/phi-2-psy
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inference: false
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license: mit
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model_creator: vince62s
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model_name: phi-2-psy
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pipeline_tag: text-generation
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quantized_by: afrideva
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tags:
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- merge
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- mergekit
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- lazymergekit
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- rhysjones/phi-2-orange
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- cognitivecomputations/dolphin-2_6-phi-2
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- gguf
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- ggml
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- quantized
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- q2_k
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- q3_k_m
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- q4_k_m
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- q5_k_m
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- q6_k
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- q8_0
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---
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# vince62s/phi-2-psy-GGUF
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Quantized GGUF model files for [phi-2-psy](https://huggingface.co/vince62s/phi-2-psy) from [vince62s](https://huggingface.co/vince62s)
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| Name | Quant method | Size |
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| ---- | ---- | ---- |
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| [phi-2-psy.fp16.gguf](https://huggingface.co/afrideva/phi-2-psy-GGUF/resolve/main/phi-2-psy.fp16.gguf) | fp16 | 5.56 GB |
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| [phi-2-psy.q2_k.gguf](https://huggingface.co/afrideva/phi-2-psy-GGUF/resolve/main/phi-2-psy.q2_k.gguf) | q2_k | 1.11 GB |
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| [phi-2-psy.q3_k_m.gguf](https://huggingface.co/afrideva/phi-2-psy-GGUF/resolve/main/phi-2-psy.q3_k_m.gguf) | q3_k_m | 1.43 GB |
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| [phi-2-psy.q4_k_m.gguf](https://huggingface.co/afrideva/phi-2-psy-GGUF/resolve/main/phi-2-psy.q4_k_m.gguf) | q4_k_m | 1.74 GB |
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| [phi-2-psy.q5_k_m.gguf](https://huggingface.co/afrideva/phi-2-psy-GGUF/resolve/main/phi-2-psy.q5_k_m.gguf) | q5_k_m | 2.00 GB |
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| [phi-2-psy.q6_k.gguf](https://huggingface.co/afrideva/phi-2-psy-GGUF/resolve/main/phi-2-psy.q6_k.gguf) | q6_k | 2.29 GB |
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| [phi-2-psy.q8_0.gguf](https://huggingface.co/afrideva/phi-2-psy-GGUF/resolve/main/phi-2-psy.q8_0.gguf) | q8_0 | 2.96 GB |
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## Original Model Card:
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# Phi-2-psy
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Phi-2-psy is a merge of the following models:
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* [rhysjones/phi-2-orange](https://huggingface.co/rhysjones/phi-2-orange)
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* [cognitivecomputations/dolphin-2_6-phi-2](https://huggingface.co/cognitivecomputations/dolphin-2_6-phi-2)
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## 🏆 Evaluation
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The evaluation was performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval) on Nous suite.
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| Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
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|----------------------------------------------------------------|------:|------:|---------:|-------:|------:|
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|[**phi-2-psy**](https://huggingface.co/vince62s/phi-2-psy)| **34.4**| **71.4**| **48.2**| **38.1**| **48.02**|
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|[phixtral-2x2_8](https://huggingface.co/mlabonne/phixtral-2x2_8)| 34.1| 70.4| 48.8| 37.8| 47.78|
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|[dolphin-2_6-phi-2](https://huggingface.co/cognitivecomputations/dolphin-2_6-phi-2)| 33.1| 69.9| 47.4| 37.2| 46.89|
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|[phi-2-orange](https://huggingface.co/rhysjones/phi-2-orange)| 33.4| 71.3| 49.9| 37.3| 47.97|
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|[phi-2](https://huggingface.co/microsoft/phi-2)| 28.0| 70.8| 44.4| 35.2| 44.61|
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## 🧩 Configuration
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```yaml
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slices:
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- sources:
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- model: rhysjones/phi-2-orange
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layer_range: [0, 32]
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- model: cognitivecomputations/dolphin-2_6-phi-2
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layer_range: [0, 32]
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merge_method: slerp
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base_model: rhysjones/phi-2-orange
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parameters:
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t:
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- filter: self_attn
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value: [0, 0.5, 0.3, 0.7, 1]
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- filter: mlp
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value: [1, 0.5, 0.7, 0.3, 0]
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- value: 0.5
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dtype: bfloat16
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```
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## 💻 Usage
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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torch.set_default_device("cuda")
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model = AutoModelForCausalLM.from_pretrained("vince62s/phi-2-psy", torch_dtype="auto", trust_remote_code=True)
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tokenizer = AutoTokenizer.from_pretrained("vince62s/phi-2-psy", trust_remote_code=True)
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inputs = tokenizer('''def print_prime(n):
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"""
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Print all primes between 1 and n
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"""''', return_tensors="pt", return_attention_mask=False)
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outputs = model.generate(**inputs, max_length=200)
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text = tokenizer.batch_decode(outputs)[0]
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print(text)
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```
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