Text Generation
Transformers
Safetensors
English
spike_whale
feature-extraction
small-models
base-model
mla
jepa
experimental
custom_code
Instructions to use Quazim0t0/Escarda-86M-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Quazim0t0/Escarda-86M-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Quazim0t0/Escarda-86M-Base", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Quazim0t0/Escarda-86M-Base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Quazim0t0/Escarda-86M-Base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Quazim0t0/Escarda-86M-Base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Quazim0t0/Escarda-86M-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Quazim0t0/Escarda-86M-Base
- SGLang
How to use Quazim0t0/Escarda-86M-Base 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 "Quazim0t0/Escarda-86M-Base" \ --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": "Quazim0t0/Escarda-86M-Base", "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 "Quazim0t0/Escarda-86M-Base" \ --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": "Quazim0t0/Escarda-86M-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Quazim0t0/Escarda-86M-Base with Docker Model Runner:
docker model run hf.co/Quazim0t0/Escarda-86M-Base
Upload tokenizer_config.json with huggingface_hub
Browse files- tokenizer_config.json +204 -198
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"<|im_end|>",
|
| 190 |
+
"<|im_start|>",
|
| 191 |
+
"<|system|>",
|
| 192 |
+
"<|user|>"
|
| 193 |
+
],
|
| 194 |
+
"model_max_length": 1000000000000000019884624838656,
|
| 195 |
+
"pad_token": "<pad>",
|
| 196 |
+
"tokenizer_class": "SpikeTokenizer",
|
| 197 |
+
"unk_token": "<unk>",
|
| 198 |
+
"auto_map": {
|
| 199 |
+
"AutoTokenizer": [
|
| 200 |
+
"spike_tokenizer.SpikeTokenizer",
|
| 201 |
+
null
|
| 202 |
+
]
|
| 203 |
+
}
|
| 204 |
+
}
|