Text Generation
Transformers
PyTorch
Safetensors
English
custom_gpt
GPT
GPT-3 Small
GPT-3 Medium
GPT-3 Large
GPT-3 XL
GPT-3 2.7B
GPT-3 6.7B
GPT-3 13B
GPT-3 175B
GPT-3
GPT-2
GPT-2 124M
mit
HuggingFace
fineweb-edu
Decoder-Only
custom_code
Instructions to use samkeet/GPT_124M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use samkeet/GPT_124M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="samkeet/GPT_124M", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("samkeet/GPT_124M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use samkeet/GPT_124M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "samkeet/GPT_124M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "samkeet/GPT_124M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/samkeet/GPT_124M
- SGLang
How to use samkeet/GPT_124M 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 "samkeet/GPT_124M" \ --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": "samkeet/GPT_124M", "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 "samkeet/GPT_124M" \ --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": "samkeet/GPT_124M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use samkeet/GPT_124M with Docker Model Runner:
docker model run hf.co/samkeet/GPT_124M
Update README.md
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README.md
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## Model Sources
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- **Paper:** [Language Models are Unsupervised Multitask Learners](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
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- **Paper:** [Language
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- **Demo:** [More Information Needed]
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## Model Details
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- **Dataset:** `fineweb-edu` (10 billion tokens)
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- **Training Date:** `January 2025`
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- **Validation Dataset:** 100 million tokens of HuggingFaceFW/fineweb-edu
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## Usage
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### Direct Use
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## Model Sources
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- **Paper:** [Language Models are Unsupervised Multitask Learners](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
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- **Paper:** [Language Models are Few-Shot Learners](https://arxiv.org/pdf/2005.14165)
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- **Paper:** [Training Compute-Optimal Large Language Models](https://arxiv.org/pdf/2203.15556)
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- **Video:** [Andrej Karpathy-Let's reproduce GPT-2 (124M)](https://youtu.be/l8pRSuU81PU?si=KAo1y9dHYQAGJmj5)
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- **Demo:** [More Information Needed]
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## Model Details
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- **Dataset:** `fineweb-edu` (10 billion tokens)
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- **Training Date:** `January 2025`
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- **Validation Dataset:** 100 million tokens of HuggingFaceFW/fineweb-edu
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## Usage
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### Direct Use
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