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---
license: other
tags:
- yi
- moe
license_name: yi-license
license_link: https://huggingface.co./01-ai/Yi-34B-200K/blob/main/LICENSE
model-index:
- name: Cosmosis-3x34B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 69.71
name: normalized accuracy
source:
url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Cosmosis-3x34B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 85.18
name: normalized accuracy
source:
url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Cosmosis-3x34B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 77.25
name: accuracy
source:
url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Cosmosis-3x34B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 63.82
source:
url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Cosmosis-3x34B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 84.14
name: accuracy
source:
url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Cosmosis-3x34B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 72.25
name: accuracy
source:
url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Cosmosis-3x34B
name: Open LLM Leaderboard
---
![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6468ce47e134d050a58aa89c/jVCgVixLmOsAofXVUUgkg.jpeg)
# Cosmosis-3x34B
This is the model for Cosmosis-3x34B. I used [this repo](https://bit.ly/weyaxi-moe-repo) to make this MOE model.
# Prompt Template(s):
Since [bagel-dpo-34b-v0.2](https://huggingface.co./jondurbin/bagel-dpo-34b-v0.2) uses many prompt templates, you can utilize prompt templates provided by bagel and other expert's prompt templates.
**Note:** I currently do not know which prompt template is best.
### ChatML:
```
<|im_start|>system
{system}<|im_end|>
<|im_start|>user
{user}<|im_end|>
<|im_start|>assistant
{asistant}<|im_end|>
```
### Human Asistant
```
Human: {user}
### Assistant: {asistant}
```
### Alpaca (sort of)
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{system}
{instruction}
### Response:
```
### Vicuna
```
{system}
USER: {instruction}
ASSISTANT:
```
Visit [bagel-dpo-34b-v0.2](https://huggingface.co./jondurbin/bagel-dpo-34b-v0.2) to try more prompt templates.
# Yaml Config to reproduce
```yaml
base_model: nontoxic-bagel-34b-v0.2
gate_mode: hidden
dtype: bfloat16
experts:
- source_model: bagel-dpo-34b-v0.2
positive_prompts: ["question answering", "Q:", science", "biology", "chemistry", "physics"]
negative_prompts: ["math", "reason", "mathematics", "solve", "count", "code", "python", "javascript", "programming", "algorithm"]
- source_model: Nous-Hermes-2-Yi-34B
positive_prompts: ["chat", "math", "reason", "mathematics", "solve", "count", "python", "javascript", "programming", "algorithm", "tell me", "assistant"]
- source_model: SUS-Chat-34B
positive_prompts: ["math", "reason", "mathematics", "solve", "count", "assistant"]
```
# Quantizationed versions
Quantizationed versions of this model is available thanks to [TheBloke](https://hf.co/TheBloke).
##### GPTQ
- [TheBloke/Cosmosis-3x34B-GPTQ](https://huggingface.co./TheBloke/Cosmosis-3x34B-GPTQ)
##### GGUF
- [TheBloke/Cosmosis-3x34B-GGUF](https://huggingface.co./TheBloke/Cosmosis-3x34B-GGUF)
##### AWQ
- [TheBloke/Cosmosis-3x34B-AWQ](https://huggingface.co./TheBloke/Cosmosis-3x34B-AWQ)
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co./datasets/open-llm-leaderboard/details_Weyaxi__Cosmosis-3x34B)
| Metric |Value|
|---------------------------------|----:|
|Avg. |75.39|
|AI2 Reasoning Challenge (25-Shot)|69.71|
|HellaSwag (10-Shot) |85.18|
|MMLU (5-Shot) |77.25|
|TruthfulQA (0-shot) |63.82|
|Winogrande (5-shot) |84.14|
|GSM8k (5-shot) |72.25|
If you would like to support me:
[☕ Buy Me a Coffee](https://www.buymeacoffee.com/weyaxi) |