Instructions to use acrastt/Marx-3B-V3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use acrastt/Marx-3B-V3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="acrastt/Marx-3B-V3", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("acrastt/Marx-3B-V3", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use acrastt/Marx-3B-V3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "acrastt/Marx-3B-V3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "acrastt/Marx-3B-V3", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/acrastt/Marx-3B-V3
- SGLang
How to use acrastt/Marx-3B-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 "acrastt/Marx-3B-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": "acrastt/Marx-3B-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 "acrastt/Marx-3B-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": "acrastt/Marx-3B-V3", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use acrastt/Marx-3B-V3 with Docker Model Runner:
docker model run hf.co/acrastt/Marx-3B-V3
Upload modeling_stablelm_epoch.py
Browse files
modeling_stablelm_epoch.py
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@@ -561,10 +561,10 @@ class StableLMEpochForCausalLM(StableLMEpochPreTrainedModel):
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self.lm_head = new_embeddings
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def get_decoder(self):
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def set_decoder(self, decoder):
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self.
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def forward(
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self,
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self.lm_head = new_embeddings
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def get_decoder(self):
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return self.model
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def set_decoder(self, decoder):
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self.model = decoder
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def forward(
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self,
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