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RichardErkhov/g-ronimo_-_phi-2-OpenHermes-2.5-gguf
g-ronimo_-_phi-2-OpenHermes-2.5-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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From the Hugging Face model README
Quantization made by Richard Erkhov.
phi-2-OpenHermes-2.5 - GGUF
| Name | Quant method | Size |
|---|---|---|
| phi-2-OpenHermes-2.5.Q2_K.gguf | Q2_K | 1.03GB |
| phi-2-OpenHermes-2.5.IQ3_XS.gguf | IQ3_XS | 1.12GB |
| phi-2-OpenHermes-2.5.IQ3_S.gguf | IQ3_S | 1.16GB |
| phi-2-OpenHermes-2.5.Q3_K_S.gguf | Q3_K_S | 1.16GB |
| phi-2-OpenHermes-2.5.IQ3_M.gguf | IQ3_M | 1.23GB |
| phi-2-OpenHermes-2.5.Q3_K.gguf | Q3_K | 1.33GB |
| phi-2-OpenHermes-2.5.Q3_K_M.gguf | Q3_K_M | 1.33GB |
| phi-2-OpenHermes-2.5.Q3_K_L.gguf | Q3_K_L | 1.47GB |
| phi-2-OpenHermes-2.5.IQ4_XS.gguf | IQ4_XS | 1.43GB |
| phi-2-OpenHermes-2.5.Q4_0.gguf | Q4_0 | 1.49GB |
| phi-2-OpenHermes-2.5.IQ4_NL.gguf | IQ4_NL | 1.5GB |
| phi-2-OpenHermes-2.5.Q4_K_S.gguf | Q4_K_S | 1.51GB |
| phi-2-OpenHermes-2.5.Q4_K.gguf | Q4_K | 1.62GB |
| phi-2-OpenHermes-2.5.Q4_K_M.gguf | Q4_K_M | 1.62GB |
| phi-2-OpenHermes-2.5.Q4_1.gguf | Q4_1 | 1.65GB |
| phi-2-OpenHermes-2.5.Q5_0.gguf | Q5_0 | 1.8GB |
| phi-2-OpenHermes-2.5.Q5_K_S.gguf | Q5_K_S | 1.8GB |
| phi-2-OpenHermes-2.5.Q5_K.gguf | Q5_K | 1.87GB |
| phi-2-OpenHermes-2.5.Q5_K_M.gguf | Q5_K_M | 1.87GB |
| phi-2-OpenHermes-2.5.Q5_1.gguf | Q5_1 | 1.95GB |
| phi-2-OpenHermes-2.5.Q6_K.gguf | Q6_K | 2.13GB |
| phi-2-OpenHermes-2.5.Q8_0.gguf | Q8_0 | 2.75GB |
library_name: transformers license: mit datasets:
| Model | AGIEval | GPT4All | TruthfulQA | Bigbench | Average |
|---|---|---|---|---|---|
| g-ronimo/phi-2-OpenHermes-2.5 | 30.27 | 71.18 | 43.87 | 35.9 | 45.3 |
| minghaowu/phi-2-OpenHermes-2.5 | 27.95 | 67.55 | 48.07 | 36.17 | 44.94 |
| phi-2 | 27.96 | 70.84 | 44.46 | 35.17 | 44.61 |
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
modelpath="g-ronimo/phi-2-OpenHermes-2.5"
model = AutoModelForCausalLM.from_pretrained(
modelpath,
torch_dtype=torch.bfloat16,
device_map="auto",
# attn_implementation="flash_attention_2",
)
tokenizer = AutoTokenizer.from_pretrained(modelpath)
messages = [
{"role": "system", "content": "answer like a pirate"},
{"role": "user", "content": "what does it mean to be successful?"},
]
input_tokens = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to("cuda")
output_tokens = model.generate(input_tokens, max_new_tokens=500)
output = tokenizer.decode(output_tokens[0])
print(output)
Ahoy there, matey! To me, being successful means having the wind in your sails and reaching the treasure you've been dreaming of. It's about setting sail on a journey with clear goals, working hard, facing challenges head-on, and never losing sight of what truly matters. So, set your compass right, hoist your Jolly Roger high, and let's embark on this adventure together! ⚓️💰⛵️