|
--- |
|
license: apache-2.0 |
|
tags: |
|
- moe |
|
- merge |
|
- mergekit |
|
- kodonho/SolarM-SakuraSolar-SLERP |
|
- Sao10K/Sensualize-Solar-10.7B |
|
- NousResearch/Nous-Hermes-2-SOLAR-10.7B |
|
- fblgit/UNA-SOLAR-10.7B-Instruct-v1.0 |
|
--- |
|
|
|
![image/png](https://cdn-uploads.huggingface.co/production/uploads/64545af5ec40bbbd01242ca6/TN6IeT8hHjMCVljzVn2Fs.png) |
|
|
|
# Umbra-MoE-4x10.7 |
|
|
|
Umbra is an off shoot of the [Lumosia Series] with a Focus in General Knowlage and RP/ERP |
|
|
|
This model was built around the idea someone wanted a General Assiatant that could also tell Stories/RP/ERP when wanted. |
|
|
|
This is a very experimantal model. Its a combination MoE of Solar models,Models slected are based off of personal experiance not open leaderboard. |
|
|
|
context is 4k |
|
|
|
Please let me know how the model works for you. |
|
|
|
Template: ChatML |
|
``` |
|
### System: |
|
|
|
### USER:{prompt} |
|
|
|
### Assistant: |
|
``` |
|
|
|
|
|
Settings: |
|
``` |
|
Temp: 1.0 |
|
min-p: 0.02-0.1 |
|
``` |
|
|
|
## Evals: |
|
|
|
Will post after Benchmark |
|
|
|
* Avg: |
|
* ARC: |
|
* HellaSwag: |
|
* MMLU: |
|
* T-QA: |
|
* Winogrande: |
|
* GSM8K: |
|
|
|
## Examples: |
|
``` |
|
To Come |
|
``` |
|
``` |
|
To Come |
|
``` |
|
|
|
Umbra-MoE-4x10.7 is a Mixure of Experts (MoE) made with the following models using: |
|
* [kodonho/SolarM-SakuraSolar-SLERP](https://huggingface.co/kodonho/SolarM-SakuraSolar-SLERP) |
|
* [Sao10K/Sensualize-Solar-10.7B](https://huggingface.co/Sao10K/Sensualize-Solar-10.7B) |
|
* [NousResearch/Nous-Hermes-2-SOLAR-10.7B](https://huggingface.co/NousResearch/Nous-Hermes-2-SOLAR-10.7B) |
|
* [fblgit/UNA-SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/fblgit/UNA-SOLAR-10.7B-Instruct-v1.0) |
|
|
|
## 🧩 Configuration |
|
|
|
``` |
|
base_model: kodonho/SolarM-SakuraSolar-SLERP |
|
gate_mode: hidden |
|
dtype: bfloat16 |
|
experts: |
|
- source_model: kodonho/SolarM-SakuraSolar-SLERP |
|
positive_prompts: |
|
- "versatile" |
|
- "helpful" |
|
- "factual" |
|
- "integrated" |
|
- "adaptive" |
|
- "comprehensive" |
|
- "balanced" |
|
negative_prompts: |
|
- "specialized" |
|
- "narrow" |
|
- "focused" |
|
- "limited" |
|
- "specific" |
|
|
|
- source_model: Sao10K/Sensualize-Solar-10.7B |
|
positive_prompts: |
|
- "creative" |
|
- "chat" |
|
- "discuss" |
|
- "culture" |
|
- "world" |
|
- "expressive" |
|
- "detailed" |
|
- "imaginative" |
|
- "engaging" |
|
negative_prompts: |
|
- "sorry" |
|
- "cannot" |
|
- "factual" |
|
- "concise" |
|
- "straightforward" |
|
- "objective" |
|
- "dry" |
|
|
|
- source_model: NousResearch/Nous-Hermes-2-SOLAR-10.7B |
|
positive_prompts: |
|
- "analytical" |
|
- "accurate" |
|
- "logical" |
|
- "knowledgeable" |
|
- "precise" |
|
- "calculate" |
|
- "compute" |
|
- "solve" |
|
- "work" |
|
- "python" |
|
- "javascript" |
|
- "programming" |
|
- "algorithm" |
|
- "tell me" |
|
- "assistant" |
|
negative_prompts: |
|
- "creative" |
|
- "abstract" |
|
- "imaginative" |
|
- "artistic" |
|
- "emotional" |
|
- "mistake" |
|
- "inaccurate" |
|
|
|
- source_model: fblgit/UNA-SOLAR-10.7B-Instruct-v1.0 |
|
positive_prompts: |
|
- "instructive" |
|
- "clear" |
|
- "directive" |
|
- "helpful" |
|
- "informative" |
|
negative_prompts: |
|
- "exploratory" |
|
- "open-ended" |
|
- "narrative" |
|
- "speculative" |
|
- "artistic" |
|
``` |
|
|
|
## 💻 Usage |
|
|
|
```python |
|
!pip install -qU transformers bitsandbytes accelerate |
|
|
|
from transformers import AutoTokenizer |
|
import transformers |
|
import torch |
|
|
|
model = "Steelskull/Umbra-MoE-4x10.7" |
|
|
|
tokenizer = AutoTokenizer.from_pretrained(model) |
|
pipeline = transformers.pipeline( |
|
"text-generation", |
|
model=model, |
|
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True}, |
|
) |
|
|
|
messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}] |
|
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
|
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"]) |
|
``` |