Text Generation
Transformers
Safetensors
gpt2
Generated from Trainer
Eval Results (legacy)
text-generation-inference
Instructions to use nomsgadded/clm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nomsgadded/clm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nomsgadded/clm")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("nomsgadded/clm") model = AutoModelForCausalLM.from_pretrained("nomsgadded/clm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use nomsgadded/clm with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nomsgadded/clm" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nomsgadded/clm", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/nomsgadded/clm
- SGLang
How to use nomsgadded/clm 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 "nomsgadded/clm" \ --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": "nomsgadded/clm", "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 "nomsgadded/clm" \ --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": "nomsgadded/clm", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use nomsgadded/clm with Docker Model Runner:
docker model run hf.co/nomsgadded/clm
Commit ·
a764d9f
1
Parent(s): ba7457a
End of training
Browse files- README.md +2 -2
- all_results.json +15 -0
- eval_results.json +10 -0
- train_results.json +8 -0
- trainer_state.json +79 -0
README.md
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name: Causal Language Modeling
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type: text-generation
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dataset:
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name: wikitext
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type: wikitext
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config: wikitext-2-raw-v1
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split: validation
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# clm
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This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the wikitext dataset.
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It achieves the following results on the evaluation set:
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- Loss: 3.4802
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- Accuracy: 0.3719
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name: Causal Language Modeling
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type: text-generation
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dataset:
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name: wikitext wikitext-2-raw-v1
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type: wikitext
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config: wikitext-2-raw-v1
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split: validation
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# clm
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This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the wikitext wikitext-2-raw-v1 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 3.4802
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- Accuracy: 0.3719
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all_results.json
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{
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"epoch": 0.99,
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"eval_accuracy": 0.37187601824698596,
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"eval_loss": 3.4801909923553467,
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"eval_runtime": 104.7921,
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"eval_samples": 240,
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"eval_samples_per_second": 2.29,
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"eval_steps_per_second": 0.286,
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"perplexity": 32.46592222670883,
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"train_loss": 3.7061044375101724,
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"train_runtime": 2296.6495,
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"train_samples": 2318,
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"train_samples_per_second": 1.009,
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"train_steps_per_second": 0.031
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}
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eval_results.json
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{
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"epoch": 0.99,
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"eval_accuracy": 0.37187601824698596,
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"eval_loss": 3.4801909923553467,
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"eval_runtime": 104.7921,
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"eval_samples": 240,
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"eval_samples_per_second": 2.29,
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"eval_steps_per_second": 0.286,
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"perplexity": 32.46592222670883
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}
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train_results.json
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{
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"epoch": 0.99,
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"train_loss": 3.7061044375101724,
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"train_runtime": 2296.6495,
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"train_samples": 2318,
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"train_samples_per_second": 1.009,
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"train_steps_per_second": 0.031
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}
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trainer_state.json
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{
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"best_metric": 0.37187601824698596,
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"best_model_checkpoint": ".\\output\\checkpoint-72",
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"is_hyper_param_search": false,
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"is_local_process_zero": true,
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"is_world_process_zero": true,
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"log_history": [
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"learning_rate": 2.90625e-05,
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}
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