Text Generation
Transformers
Safetensors
English
cloverlm
causal-lm
quartet-ii
nvfp4
low-precision-training
pretrained
custom_code
Instructions to use daslab-testing/CloverLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use daslab-testing/CloverLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="daslab-testing/CloverLM", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("daslab-testing/CloverLM", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use daslab-testing/CloverLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "daslab-testing/CloverLM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "daslab-testing/CloverLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/daslab-testing/CloverLM
- SGLang
How to use daslab-testing/CloverLM 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 "daslab-testing/CloverLM" \ --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": "daslab-testing/CloverLM", "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 "daslab-testing/CloverLM" \ --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": "daslab-testing/CloverLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use daslab-testing/CloverLM with Docker Model Runner:
docker model run hf.co/daslab-testing/CloverLM
Upload folder using huggingface_hub
Browse files- config.json +5 -5
- modeling_cloverlm.py +1 -1
config.json
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]
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},
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"d_head": 128,
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"heads": 28,
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"hidden_size": 3584,
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"intermediate_size": 14336,
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"num_blocks": 29,
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"num_hidden_layers": 29,
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"num_key_value_heads": 7,
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"quartet_2_impl": "pseudoquant",
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"ratio": 4,
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"scale_type": "1/sqrt(d)",
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"head_dim": 128,
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"tie_word_embeddings": true,
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"transformers_version": "5.3.0",
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"vocab_size": 32000,
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"weight_tying": true
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"quantization_config": {
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"quant_method": "quartet2"
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}
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}
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]
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},
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"d_head": 128,
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"head_dim": 128,
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"heads": 28,
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"hidden_size": 3584,
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"intermediate_size": 14336,
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"num_blocks": 29,
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"num_hidden_layers": 29,
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"num_key_value_heads": 7,
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"quantization_config": {
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"quant_method": "quartet2"
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},
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"quartet_2_impl": "pseudoquant",
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"ratio": 4,
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"scale_type": "1/sqrt(d)",
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"tie_word_embeddings": true,
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"transformers_version": "5.3.0",
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"vocab_size": 32000,
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"weight_tying": true
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}
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modeling_cloverlm.py
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return {"input_ids": input_ids}
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def _supports_default_dynamic_cache(self):
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return False
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return {"input_ids": input_ids}
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def _supports_default_dynamic_cache(self):
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return False
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