Instructions to use DMxObito/EduSolver with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use DMxObito/EduSolver with PEFT:
Task type is invalid.
- Transformers
How to use DMxObito/EduSolver with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="DMxObito/EduSolver")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("DMxObito/EduSolver") model = AutoModelForCausalLM.from_pretrained("DMxObito/EduSolver") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use DMxObito/EduSolver with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DMxObito/EduSolver" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DMxObito/EduSolver", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/DMxObito/EduSolver
- SGLang
How to use DMxObito/EduSolver 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 "DMxObito/EduSolver" \ --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": "DMxObito/EduSolver", "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 "DMxObito/EduSolver" \ --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": "DMxObito/EduSolver", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use DMxObito/EduSolver with Docker Model Runner:
docker model run hf.co/DMxObito/EduSolver
Upload folder using huggingface_hub
Browse files- config.json +33 -0
- generation_config.json +6 -0
- model.safetensors +3 -0
config.json
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{
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"architectures": [
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"PhiForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 50256,
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"dtype": "float16",
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"embd_pdrop": 0.0,
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"eos_token_id": 50256,
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"hidden_act": "gelu_new",
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"hidden_size": 2560,
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"initializer_range": 0.02,
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"intermediate_size": 10240,
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 2048,
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"model_type": "phi",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 32,
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"pad_token_id": 50256,
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"partial_rotary_factor": 0.4,
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"qk_layernorm": false,
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"resid_pdrop": 0.1,
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"rope_parameters": {
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"partial_rotary_factor": 0.4,
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"rope_theta": 10000.0,
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"rope_type": "default"
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},
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"tie_word_embeddings": false,
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"transformers_version": "5.0.0",
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"use_cache": true,
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"vocab_size": 51200
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 50256,
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"eos_token_id": 50256,
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"transformers_version": "5.0.0"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:51d49af9a9ce9c42cc9377e7c55657d6781c0cd9edeb5b095b1b14ab4b9ad879
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size 5559417456
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