ColdBrew-Indium / README.md
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---
base_model:
- SvalTek/ColdBrew-Aphid
- SvalTek/ColdBrew-Aphid
- SvalTek/ColdBrew-Aphid
- SvalTek/ColdBrew-Aphid
- SvalTek/ColdBrew-Aphid
- SvalTek/ColdBrew-Aphid
tags:
- merge
- mergekit
- lazymergekit
- SvalTek/ColdBrew-Aphid
---
# ColdBrew-Indium
ColdBrew-Indium is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [SvalTek/ColdBrew-Aphid](https://huggingface.co./SvalTek/ColdBrew-Aphid)
* [SvalTek/ColdBrew-Aphid](https://huggingface.co./SvalTek/ColdBrew-Aphid)
* [SvalTek/ColdBrew-Aphid](https://huggingface.co./SvalTek/ColdBrew-Aphid)
* [SvalTek/ColdBrew-Aphid](https://huggingface.co./SvalTek/ColdBrew-Aphid)
* [SvalTek/ColdBrew-Aphid](https://huggingface.co./SvalTek/ColdBrew-Aphid)
* [SvalTek/ColdBrew-Aphid](https://huggingface.co./SvalTek/ColdBrew-Aphid)
## 🧩 Configuration
```yaml
const_tag: &scale_factor 0.7071067812 # 1/sqrt(2) scaling for stability
attenuate-env: &attenuated_env
parameters:
scale:
- filter: q_proj
value: *scale_factor
- filter: k_proj
value: *scale_factor
- filter: v_proj
value: *scale_factor
- filter: o_proj
value: *scale_factor
- value: 1.0
slices:
# Preserve input layers
- sources:
- model: SvalTek/ColdBrew-Aphid
layer_range: [0, 8]
# expand upper layers
- sources:
- model: SvalTek/ColdBrew-Aphid
layer_range: [8, 16]
- sources:
- model: SvalTek/ColdBrew-Aphid
layer_range: [8, 16]
<<: *attenuated_env
# expand upper layers
- sources:
- model: SvalTek/ColdBrew-Aphid
layer_range: [16, 22]
- sources:
- model: SvalTek/ColdBrew-Aphid
layer_range: [16, 22]
<<: *attenuated_env
# Preserve output layers
- sources:
- model: SvalTek/ColdBrew-Aphid
layer_range: [22, 27]
merge_method: passthrough
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "SvalTek/ColdBrew-Indium"
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"])
```