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RichardErkhov/
Samee-ur_-_NeuralPipe-7B-slerp-DPO-gguf

Name: Samee-ur_-_NeuralPipe-7B-slerp-DPO-gguf
Availability: InStock

endpoints_compatible

gguf

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Quantization made by Richard Erkhov.

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NeuralPipe-7B-slerp-DPO - GGUF

Model creator: https://huggingface.co/Samee-ur/
Original model: https://huggingface.co/Samee-ur/NeuralPipe-7B-slerp-DPO/

Name	Quant method	Size
NeuralPipe-7B-slerp-DPO.Q2_K.gguf	Q2_K	2.53GB
NeuralPipe-7B-slerp-DPO.IQ3_XS.gguf	IQ3_XS	2.81GB
NeuralPipe-7B-slerp-DPO.IQ3_S.gguf	IQ3_S	2.96GB
NeuralPipe-7B-slerp-DPO.Q3_K_S.gguf	Q3_K_S	2.95GB
NeuralPipe-7B-slerp-DPO.IQ3_M.gguf	IQ3_M	3.06GB
NeuralPipe-7B-slerp-DPO.Q3_K.gguf	Q3_K	3.28GB
NeuralPipe-7B-slerp-DPO.Q3_K_M.gguf	Q3_K_M	3.28GB
NeuralPipe-7B-slerp-DPO.Q3_K_L.gguf	Q3_K_L	3.56GB
NeuralPipe-7B-slerp-DPO.IQ4_XS.gguf	IQ4_XS	3.67GB
NeuralPipe-7B-slerp-DPO.Q4_0.gguf	Q4_0	3.83GB
NeuralPipe-7B-slerp-DPO.IQ4_NL.gguf	IQ4_NL	3.87GB
NeuralPipe-7B-slerp-DPO.Q4_K_S.gguf	Q4_K_S	3.86GB
NeuralPipe-7B-slerp-DPO.Q4_K.gguf	Q4_K	4.07GB
NeuralPipe-7B-slerp-DPO.Q4_K_M.gguf	Q4_K_M	4.07GB
NeuralPipe-7B-slerp-DPO.Q4_1.gguf	Q4_1	4.24GB
NeuralPipe-7B-slerp-DPO.Q5_0.gguf	Q5_0	4.65GB
NeuralPipe-7B-slerp-DPO.Q5_K_S.gguf	Q5_K_S	4.65GB
NeuralPipe-7B-slerp-DPO.Q5_K.gguf	Q5_K	4.78GB
NeuralPipe-7B-slerp-DPO.Q5_K_M.gguf	Q5_K_M	4.78GB
NeuralPipe-7B-slerp-DPO.Q5_1.gguf	Q5_1	5.07GB
NeuralPipe-7B-slerp-DPO.Q6_K.gguf	Q6_K	5.53GB
NeuralPipe-7B-slerp-DPO.Q8_0.gguf	Q8_0	7.17GB

Original model description:

license: apache-2.0 datasets:

Intel/orca_dpo_pairs language:
en

NeuralPipe-7B-slerp-DPO

NeuralPipe-7B-slerp is a Direct Preference Optimized version of Samee-ur/NeuralPipe-7B-slerp. I performed Direct Preference Optimization on the Intel/orca_dpo_pairs dataset

💻 Usage

python

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Samee-ur/NeuralPipe-7B-slerp-DPO"
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"])

RichardErkhov/Samee-ur_-_NeuralPipe-7B-slerp-DPO-gguf

NeuralPipe-7B-slerp-DPO

💻 Usage

RichardErkhov/
Samee-ur_-_NeuralPipe-7B-slerp-DPO-gguf