Text Generation
Transformers
Safetensors
mistral
Merge
mergekit
lazymergekit
liminerity/M7-7b
Kukedlc/Neural4gsm8k
Kukedlc/Jupiter-k-7B-slerp
Kukedlc/NeuralMaxime-7B-slerp
Kukedlc/NeuralFusion-7b-Dare-Ties
Kukedlc/Neural-Krishna-Multiverse-7b-v3
Kukedlc/NeuTrixOmniBe-DPO
Kukedlc/NeuralSirKrishna-7b
text-generation-inference
Inference Endpoints
MyModelsMerge-7b
MyModelsMerge-7b is a merge of the following models using LazyMergekit:
- liminerity/M7-7b
- Kukedlc/Neural4gsm8k
- Kukedlc/Jupiter-k-7B-slerp
- Kukedlc/NeuralMaxime-7B-slerp
- Kukedlc/NeuralFusion-7b-Dare-Ties
- Kukedlc/Neural-Krishna-Multiverse-7b-v3
- Kukedlc/NeuTrixOmniBe-DPO
- Kukedlc/NeuralSirKrishna-7b
𧩠Configuration
models:
- model: Kukedlc/NeuralSirKrishna-7b
# no parameters necessary for base model
- model: liminerity/M7-7b
parameters:
weight: 0.1
density: 0.88
- model: Kukedlc/Neural4gsm8k
parameters:
weight: 0.1
density: 0.66
- model: Kukedlc/Jupiter-k-7B-slerp
parameters:
weight: 0.1
density: 0.66
- model: Kukedlc/NeuralMaxime-7B-slerp
parameters:
weight: 0.1
density: 0.44
- model: Kukedlc/NeuralFusion-7b-Dare-Ties
parameters:
weight: 0.1
density: 0.44
- model: Kukedlc/Neural-Krishna-Multiverse-7b-v3
parameters:
weight: 0.2
density: 0.66
- model: Kukedlc/NeuTrixOmniBe-DPO
parameters:
weight: 0.1
density: 0.33
- model: Kukedlc/NeuralSirKrishna-7b
parameters:
weight: 0.2
density: 0.88
merge_method: dare_ties
base_model: Kukedlc/NeuralSirKrishna-7b
parameters:
int8_mask: true
normalize: true
dtype: bfloat16
π» Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Kukedlc/MyModelsMerge-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"])
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