Instructions to use w-ahmad/8M-ACT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use w-ahmad/8M-ACT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="w-ahmad/8M-ACT")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("w-ahmad/8M-ACT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use w-ahmad/8M-ACT with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "w-ahmad/8M-ACT" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "w-ahmad/8M-ACT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/w-ahmad/8M-ACT
- SGLang
How to use w-ahmad/8M-ACT 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 "w-ahmad/8M-ACT" \ --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": "w-ahmad/8M-ACT", "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 "w-ahmad/8M-ACT" \ --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": "w-ahmad/8M-ACT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use w-ahmad/8M-ACT with Docker Model Runner:
docker model run hf.co/w-ahmad/8M-ACT
Training in progress, step 500
Browse files- README.md +13 -13
- config.json +1 -1
- model.safetensors +2 -2
- training_args.bin +1 -1
- training_log.jsonl +2 -2
README.md
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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#
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This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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It achieves the following results on the evaluation set:
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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### Framework versions
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tags:
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model-index:
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- name: 2M-ACT
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# 2M-ACT
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This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2182
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:------:|:----:|:---------------:|
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| 3.2722 | 0.0135 | 200 | 3.0835 |
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| 0.7555 | 0.0270 | 400 | 0.7124 |
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| 0.4207 | 0.0404 | 600 | 0.4214 |
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| 0.3327 | 0.0539 | 800 | 0.3334 |
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| 0.2918 | 0.0674 | 1000 | 0.2911 |
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| 0.2657 | 0.0809 | 1200 | 0.2649 |
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| 0.2437 | 0.0944 | 1400 | 0.2455 |
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| 0.2416 | 0.1079 | 1600 | 0.2454 |
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| 0.2257 | 0.1213 | 1800 | 0.2245 |
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| 0.2167 | 0.1348 | 2000 | 0.2182 |
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### Framework versions
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config.json
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training_args.bin
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