Quantization made by Richard Erkhov.
WestSeverus-10.7B - GGUF
- Model creator: https://huggingface.co/FelixChao/
- Original model: https://huggingface.co/FelixChao/WestSeverus-10.7B/
Name | Quant method | Size |
---|---|---|
WestSeverus-10.7B.Q2_K.gguf | Q2_K | 3.73GB |
WestSeverus-10.7B.IQ3_XS.gguf | IQ3_XS | 4.14GB |
WestSeverus-10.7B.IQ3_S.gguf | IQ3_S | 4.37GB |
WestSeverus-10.7B.Q3_K_S.gguf | Q3_K_S | 4.34GB |
WestSeverus-10.7B.IQ3_M.gguf | IQ3_M | 4.51GB |
WestSeverus-10.7B.Q3_K.gguf | Q3_K | 4.84GB |
WestSeverus-10.7B.Q3_K_M.gguf | Q3_K_M | 4.84GB |
WestSeverus-10.7B.Q3_K_L.gguf | Q3_K_L | 5.26GB |
WestSeverus-10.7B.IQ4_XS.gguf | IQ4_XS | 5.43GB |
WestSeverus-10.7B.Q4_0.gguf | Q4_0 | 5.66GB |
WestSeverus-10.7B.IQ4_NL.gguf | IQ4_NL | 5.72GB |
WestSeverus-10.7B.Q4_K_S.gguf | Q4_K_S | 5.7GB |
WestSeverus-10.7B.Q4_K.gguf | Q4_K | 6.02GB |
WestSeverus-10.7B.Q4_K_M.gguf | Q4_K_M | 6.02GB |
WestSeverus-10.7B.Q4_1.gguf | Q4_1 | 6.27GB |
WestSeverus-10.7B.Q5_0.gguf | Q5_0 | 6.89GB |
WestSeverus-10.7B.Q5_K_S.gguf | Q5_K_S | 6.89GB |
WestSeverus-10.7B.Q5_K.gguf | Q5_K | 7.08GB |
WestSeverus-10.7B.Q5_K_M.gguf | Q5_K_M | 7.08GB |
WestSeverus-10.7B.Q5_1.gguf | Q5_1 | 7.51GB |
WestSeverus-10.7B.Q6_K.gguf | Q6_K | 8.2GB |
WestSeverus-10.7B.Q8_0.gguf | Q8_0 | 10.62GB |
Original model description:
license: apache-2.0 tags: - merge - mergekit - lazymergekit - FelixChao/WestSeverus-7B-DPO-v2 - FelixChao/WestSeverus-7B-DPO-v2
WestSeverus-10.7B
WestSeverus-10.7B is a merge of the following models using LazyMergekit:
🧩 Configuration
slices:
- sources:
- model: FelixChao/WestSeverus-7B-DPO-v2
layer_range: [0, 24]
- sources:
- model: FelixChao/WestSeverus-7B-DPO-v2
layer_range: [8, 32]
merge_method: passthrough
dtype: float16
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "FelixChao/WestSeverus-10.7B"
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"])