Instructions to use Dishaaa25/adapt-dsa-tutor-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Dishaaa25/adapt-dsa-tutor-model with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "Dishaaa25/adapt-dsa-tutor-model") - Transformers
How to use Dishaaa25/adapt-dsa-tutor-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Dishaaa25/adapt-dsa-tutor-model") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Dishaaa25/adapt-dsa-tutor-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Dishaaa25/adapt-dsa-tutor-model with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Dishaaa25/adapt-dsa-tutor-model" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Dishaaa25/adapt-dsa-tutor-model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Dishaaa25/adapt-dsa-tutor-model
- SGLang
How to use Dishaaa25/adapt-dsa-tutor-model 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 "Dishaaa25/adapt-dsa-tutor-model" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Dishaaa25/adapt-dsa-tutor-model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "Dishaaa25/adapt-dsa-tutor-model" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Dishaaa25/adapt-dsa-tutor-model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Desktop
- Docker Model Runner
How to use Dishaaa25/adapt-dsa-tutor-model with Docker Model Runner:
docker model run hf.co/Dishaaa25/adapt-dsa-tutor-model
Update latest checkpoint for run 15940d1d-7d8c-4253-8810-2ea934bedee4 at step 500
Browse files- adapter_model.safetensors +1 -1
- optimizer.pt +1 -1
- rng_state.pth +1 -1
- scheduler.pt +1 -1
- trainer_state.json +1303 -3
adapter_model.safetensors
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optimizer.pt
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rng_state.pth
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scheduler.pt
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trainer_state.json
CHANGED
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"best_global_step": null,
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"best_metric": null,
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"eval_steps": 500,
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"is_local_process_zero": true,
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"rewards/reward_func/mean": 0.48919999599456787,
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"step": 450
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| 11711 |
}
|
| 11712 |
],
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| 11713 |
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| 11714 |
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| 11715 |
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| 11717 |
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| 11718 |
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| 2 |
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| 3 |
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| 4 |
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| 5 |
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| 6 |
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| 7 |
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| 8 |
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| 9 |
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| 10 |
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| 11710 |
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| 11711 |
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| 11712 |
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| 11788 |
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| 11789 |
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