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RichardErkhov/avinash31d_-_phi-2-slerp-gguf
avinash31d_-_phi-2-slerp-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.
Downloads · 30 days
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7% of all-time downloads
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.gguf37.4 GB · 100%
From the Hugging Face model README
Quantization made by Richard Erkhov.
phi-2-slerp - GGUF
| Name | Quant method | Size |
|---|---|---|
| phi-2-slerp.Q2_K.gguf | Q2_K | 1.03GB |
| phi-2-slerp.IQ3_XS.gguf | IQ3_XS | 1.12GB |
| phi-2-slerp.IQ3_S.gguf | IQ3_S | 1.16GB |
| phi-2-slerp.Q3_K_S.gguf | Q3_K_S | 1.16GB |
| phi-2-slerp.IQ3_M.gguf | IQ3_M | 1.23GB |
| phi-2-slerp.Q3_K.gguf | Q3_K | 1.33GB |
| phi-2-slerp.Q3_K_M.gguf | Q3_K_M | 1.33GB |
| phi-2-slerp.Q3_K_L.gguf | Q3_K_L | 1.47GB |
| phi-2-slerp.IQ4_XS.gguf | IQ4_XS | 1.43GB |
| phi-2-slerp.Q4_0.gguf | Q4_0 | 1.49GB |
| phi-2-slerp.IQ4_NL.gguf | IQ4_NL | 1.5GB |
| phi-2-slerp.Q4_K_S.gguf | Q4_K_S | 1.51GB |
| phi-2-slerp.Q4_K.gguf | Q4_K | 1.62GB |
| phi-2-slerp.Q4_K_M.gguf | Q4_K_M | 1.62GB |
| phi-2-slerp.Q4_1.gguf | Q4_1 | 1.65GB |
| phi-2-slerp.Q5_0.gguf | Q5_0 | 1.8GB |
| phi-2-slerp.Q5_K_S.gguf | Q5_K_S | 1.8GB |
| phi-2-slerp.Q5_K.gguf | Q5_K | 1.87GB |
| phi-2-slerp.Q5_K_M.gguf | Q5_K_M | 1.87GB |
| phi-2-slerp.Q5_1.gguf | Q5_1 | 1.95GB |
| phi-2-slerp.Q6_K.gguf | Q6_K | 2.13GB |
| phi-2-slerp.Q8_0.gguf | Q8_0 | 2.75GB |
license: mit tags:
phi-2-slerp is a merge of the following models using LazyMergekit:
slices:
- sources:
- model: microsoft/phi-2
layer_range: [0, 32]
- model: rhysjones/phi-2-orange-v2
layer_range: [0, 32]
merge_method: slerp
base_model: microsoft/phi-2
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
dtype: bfloat16
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "avinash31d/phi-2-slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])