mvpmaster/Einstein-4d-Marcoro14-nddmpk-KrishnaHercules-7b-slerp
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Model: mvpmaster/Einstein-4d-Marcoro14-nddmpk-KrishnaHercules-7b-slerp
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---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- mvpmaster/Einstein-4D-Marcoro14-7b-full-slerp
- mvpmaster/nddmp-kellemar-KrishnaHercules-7b-slerp
base_model:
- mvpmaster/Einstein-4D-Marcoro14-7b-full-slerp
- mvpmaster/nddmp-kellemar-KrishnaHercules-7b-slerp
---
# Einstein-4d-Marcoro14-nddmpk-KrishnaHercules-7b-slerp
Einstein-4d-Marcoro14-nddmpk-KrishnaHercules-7b-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [mvpmaster/Einstein-4D-Marcoro14-7b-full-slerp](https://huggingface.co/mvpmaster/Einstein-4D-Marcoro14-7b-full-slerp)
* [mvpmaster/nddmp-kellemar-KrishnaHercules-7b-slerp](https://huggingface.co/mvpmaster/nddmp-kellemar-KrishnaHercules-7b-slerp)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: mvpmaster/Einstein-4D-Marcoro14-7b-full-slerp
layer_range: [0, 32]
- model: mvpmaster/nddmp-kellemar-KrishnaHercules-7b-slerp
layer_range: [0, 32]
merge_method: slerp
base_model: mvpmaster/Einstein-4D-Marcoro14-7b-full-slerp
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mvpmaster/Einstein-4d-Marcoro14-nddmpk-KrishnaHercules-7b-slerp"
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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{
"_name_or_path": "mvpmaster/Einstein-4D-Marcoro14-7b-full-slerp",
"architectures": [
"MistralForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"eos_token_id": 2,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 32768,
"model_type": "mistral",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"rms_norm_eps": 1e-05,
"rope_theta": 10000.0,
"sliding_window": 4096,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.38.2",
"use_cache": true,
"vocab_size": 32000
}

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slices:
- sources:
- model: mvpmaster/Einstein-4D-Marcoro14-7b-full-slerp
layer_range: [0, 32]
- model: mvpmaster/nddmp-kellemar-KrishnaHercules-7b-slerp
layer_range: [0, 32]
merge_method: slerp
base_model: mvpmaster/Einstein-4D-Marcoro14-7b-full-slerp
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16

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