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RichardErkhov/Cartinoe5930_-_SOLAR-DUS-implement-gguf
Cartinoe5930_-_SOLAR-DUS-implement-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
143
22% of all-time downloads
All-time downloads
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.gguf141 GB · 100%
From the Hugging Face model README
Quantization made by Richard Erkhov.
SOLAR-DUS-implement - GGUF
| Name | Quant method | Size |
|---|---|---|
| SOLAR-DUS-implement.Q2_K.gguf | Q2_K | 3.73GB |
| SOLAR-DUS-implement.IQ3_XS.gguf | IQ3_XS | 4.14GB |
| SOLAR-DUS-implement.IQ3_S.gguf | IQ3_S | 4.37GB |
| SOLAR-DUS-implement.Q3_K_S.gguf | Q3_K_S | 4.34GB |
| SOLAR-DUS-implement.IQ3_M.gguf | IQ3_M | 4.51GB |
| SOLAR-DUS-implement.Q3_K.gguf | Q3_K | 4.84GB |
| SOLAR-DUS-implement.Q3_K_M.gguf | Q3_K_M | 4.84GB |
| SOLAR-DUS-implement.Q3_K_L.gguf | Q3_K_L | 5.26GB |
| SOLAR-DUS-implement.IQ4_XS.gguf | IQ4_XS | 5.43GB |
| SOLAR-DUS-implement.Q4_0.gguf | Q4_0 | 5.66GB |
| SOLAR-DUS-implement.IQ4_NL.gguf | IQ4_NL | 5.72GB |
| SOLAR-DUS-implement.Q4_K_S.gguf | Q4_K_S | 5.7GB |
| SOLAR-DUS-implement.Q4_K.gguf | Q4_K | 6.02GB |
| SOLAR-DUS-implement.Q4_K_M.gguf | Q4_K_M | 6.02GB |
| SOLAR-DUS-implement.Q4_1.gguf | Q4_1 | 6.27GB |
| SOLAR-DUS-implement.Q5_0.gguf | Q5_0 | 6.89GB |
| SOLAR-DUS-implement.Q5_K_S.gguf | Q5_K_S | 6.89GB |
| SOLAR-DUS-implement.Q5_K.gguf | Q5_K | 7.08GB |
| SOLAR-DUS-implement.Q5_K_M.gguf | Q5_K_M | 7.08GB |
| SOLAR-DUS-implement.Q5_1.gguf | Q5_1 | 7.51GB |
| SOLAR-DUS-implement.Q6_K.gguf | Q6_K | 8.2GB |
| SOLAR-DUS-implement.Q8_0.gguf | Q8_0 | 10.62GB |
license: apache-2.0 tags:
SOLAR-DUS-implement is a merge of the following model using LazyMergekit:
For more detailed information, please refer to GitHub Repository.
GitHub Repository: https://github.com/gauss5930/iDUS
slices:
- sources:
- model: Cartinoe5930/Llama2_init_Mistral
layer_range: [0, 24]
- sources:
- model: Cartinoe5930/Llama2_init_Mistral
layer_range: [8, 32]
merge_method: passthrough
dtype: float16
| Model | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K | Average |
|---|---|---|---|---|---|---|---|
| SOLAR-10.7B-DUS-Implementation | 59.56 | 81.18 | 63.68 | 40.72 | 76.48 | 26.99 | 58.1 |
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Cartinoe5930/SOLAR-DUS-implement"
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"])