Text Generation
Transformers
PyTorch
llama
alpaca
vicuna
uncensored
cot
chain of thought
story
adventure
roleplay
rp
Merge
mix
instruct
wizardlm
superhot
supercot
manticore
hippogriff
text-generation-inference
Instructions to use CalderaAI/30B-Epsilon with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CalderaAI/30B-Epsilon with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="CalderaAI/30B-Epsilon")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("CalderaAI/30B-Epsilon") model = AutoModelForCausalLM.from_pretrained("CalderaAI/30B-Epsilon") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use CalderaAI/30B-Epsilon with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CalderaAI/30B-Epsilon" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CalderaAI/30B-Epsilon", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/CalderaAI/30B-Epsilon
- SGLang
How to use CalderaAI/30B-Epsilon 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 "CalderaAI/30B-Epsilon" \ --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": "CalderaAI/30B-Epsilon", "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 "CalderaAI/30B-Epsilon" \ --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": "CalderaAI/30B-Epsilon", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use CalderaAI/30B-Epsilon with Docker Model Runner:
docker model run hf.co/CalderaAI/30B-Epsilon
Delete config.json
Browse files- config.json +0 -24
config.json
DELETED
|
@@ -1,24 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"_name_or_path": "hf_llama/_30B-HippoCore-Story-WLMUNC-HOT",
|
| 3 |
-
"architectures": [
|
| 4 |
-
"LlamaForCausalLM"
|
| 5 |
-
],
|
| 6 |
-
"bos_token_id": 1,
|
| 7 |
-
"eos_token_id": 2,
|
| 8 |
-
"hidden_act": "silu",
|
| 9 |
-
"hidden_size": 6656,
|
| 10 |
-
"initializer_range": 0.02,
|
| 11 |
-
"intermediate_size": 17920,
|
| 12 |
-
"max_position_embeddings": 2048,
|
| 13 |
-
"max_sequence_length": 2048,
|
| 14 |
-
"model_type": "llama",
|
| 15 |
-
"num_attention_heads": 52,
|
| 16 |
-
"num_hidden_layers": 60,
|
| 17 |
-
"pad_token_id": 0,
|
| 18 |
-
"rms_norm_eps": 1e-06,
|
| 19 |
-
"tie_word_embeddings": false,
|
| 20 |
-
"torch_dtype": "float16",
|
| 21 |
-
"transformers_version": "4.28.1",
|
| 22 |
-
"use_cache": true,
|
| 23 |
-
"vocab_size": 32000
|
| 24 |
-
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|