Text Generation
Transformers
Safetensors
mixtral
Mixture of Experts
frankenmoe
Merge
mergekit
lazymergekit
jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES
senseable/WestLake-7B-v2
mlabonne/OmniBeagle-7B
vanillaOVO/supermario_v3
Eval Results (legacy)
text-generation-inference
Instructions to use jsfs11/MixtureofMerges-MoE-4x7b-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jsfs11/MixtureofMerges-MoE-4x7b-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jsfs11/MixtureofMerges-MoE-4x7b-v3")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("jsfs11/MixtureofMerges-MoE-4x7b-v3") model = AutoModelForCausalLM.from_pretrained("jsfs11/MixtureofMerges-MoE-4x7b-v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use jsfs11/MixtureofMerges-MoE-4x7b-v3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jsfs11/MixtureofMerges-MoE-4x7b-v3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jsfs11/MixtureofMerges-MoE-4x7b-v3", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jsfs11/MixtureofMerges-MoE-4x7b-v3
- SGLang
How to use jsfs11/MixtureofMerges-MoE-4x7b-v3 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "jsfs11/MixtureofMerges-MoE-4x7b-v3" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jsfs11/MixtureofMerges-MoE-4x7b-v3", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "jsfs11/MixtureofMerges-MoE-4x7b-v3" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jsfs11/MixtureofMerges-MoE-4x7b-v3", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use jsfs11/MixtureofMerges-MoE-4x7b-v3 with Docker Model Runner:
docker model run hf.co/jsfs11/MixtureofMerges-MoE-4x7b-v3
Adding Evaluation Results
#2
by leaderboard-pr-bot - opened
README.md
CHANGED
|
@@ -215,3 +215,17 @@ Detailed results can be found [here](https://huggingface.co/datasets/open-llm-le
|
|
| 215 |
|Winogrande (5-shot) |85.00|
|
| 216 |
|GSM8k (5-shot) |68.23|
|
| 217 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 215 |
|Winogrande (5-shot) |85.00|
|
| 216 |
|GSM8k (5-shot) |68.23|
|
| 217 |
|
| 218 |
+
|
| 219 |
+
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
|
| 220 |
+
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_jsfs11__MixtureofMerges-MoE-4x7b-v3)
|
| 221 |
+
|
| 222 |
+
| Metric |Value|
|
| 223 |
+
|---------------------------------|----:|
|
| 224 |
+
|Avg. |75.31|
|
| 225 |
+
|AI2 Reasoning Challenge (25-Shot)|74.40|
|
| 226 |
+
|HellaSwag (10-Shot) |88.62|
|
| 227 |
+
|MMLU (5-Shot) |64.82|
|
| 228 |
+
|TruthfulQA (0-shot) |70.78|
|
| 229 |
+
|Winogrande (5-shot) |85.00|
|
| 230 |
+
|GSM8k (5-shot) |68.23|
|
| 231 |
+
|