Instructions to use Azrail/smallm_70_rope with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Azrail/smallm_70_rope with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Azrail/smallm_70_rope", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Azrail/smallm_70_rope", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Azrail/smallm_70_rope with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Azrail/smallm_70_rope" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Azrail/smallm_70_rope", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Azrail/smallm_70_rope
- SGLang
How to use Azrail/smallm_70_rope 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 "Azrail/smallm_70_rope" \ --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": "Azrail/smallm_70_rope", "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 "Azrail/smallm_70_rope" \ --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": "Azrail/smallm_70_rope", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Azrail/smallm_70_rope with Docker Model Runner:
docker model run hf.co/Azrail/smallm_70_rope
Training in progress, step 61000
Browse files- README.md +1 -1
- config.json +1 -5
- model.safetensors +1 -1
- training_args.bin +1 -1
README.md
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---
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library_name: transformers
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tags:
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- smallm
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model-index:
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- name: smallm_70_rope
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results: []
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---
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library_name: transformers
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tags:
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- smallm
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model-index:
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- name: smallm_70_rope
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results: []
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config.json
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],
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"attention_bias": false,
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"attention_dropout": 0.1,
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"auto_map": {
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"AutoConfig": "config.SmalLmConfig",
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"AutoModelForCausalLM": "model.SmalLmForCausalLM"
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},
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"balancing_coef": 0.0001,
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"bos_token_id": 1,
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"embedding_dropout": 0.0,
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"sliding_window_attention": true,
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"sliding_window_context": 1024,
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"sliding_window_period": 4,
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"static_residual":
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"token_experts": 3,
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"torch_dtype": "float32",
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"transformers_version": "4.50.3",
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],
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"attention_bias": false,
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"attention_dropout": 0.1,
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"balancing_coef": 0.0001,
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"bos_token_id": 1,
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"embedding_dropout": 0.0,
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"sliding_window_attention": true,
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"sliding_window_context": 1024,
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"sliding_window_period": 4,
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"static_residual": false,
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"token_experts": 3,
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"torch_dtype": "float32",
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"transformers_version": "4.50.3",
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model.safetensors
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training_args.bin
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