Instructions to use b3x0m/hyper-xomdich with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use b3x0m/hyper-xomdich with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="b3x0m/hyper-xomdich")# Load model directly from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("b3x0m/hyper-xomdich", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use b3x0m/hyper-xomdich with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "b3x0m/hyper-xomdich" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "b3x0m/hyper-xomdich", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/b3x0m/hyper-xomdich
- SGLang
How to use b3x0m/hyper-xomdich 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 "b3x0m/hyper-xomdich" \ --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": "b3x0m/hyper-xomdich", "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 "b3x0m/hyper-xomdich" \ --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": "b3x0m/hyper-xomdich", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use b3x0m/hyper-xomdich with Docker Model Runner:
docker model run hf.co/b3x0m/hyper-xomdich
Upload XomdichForConditionalGeneration
Browse files- config.json +1 -1
- pytorch_model.bin +1 -1
config.json
CHANGED
|
@@ -7,7 +7,7 @@
|
|
| 7 |
"eos_token_id": 2,
|
| 8 |
"hidden_size": 512,
|
| 9 |
"max_sequence_length": 512,
|
| 10 |
-
"model_type": "Xomdich",
|
| 11 |
"num_attention_heads": 8,
|
| 12 |
"num_key_value_heads": 4,
|
| 13 |
"num_layers": 12,
|
|
|
|
| 7 |
"eos_token_id": 2,
|
| 8 |
"hidden_size": 512,
|
| 9 |
"max_sequence_length": 512,
|
| 10 |
+
"model_type": "Hyper-Xomdich",
|
| 11 |
"num_attention_heads": 8,
|
| 12 |
"num_key_value_heads": 4,
|
| 13 |
"num_layers": 12,
|
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 471757350
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:2833061f759a4a886c5f0e0dd35a95da5776baebf51b9b6414ad942d61a88da3
|
| 3 |
size 471757350
|