Text Generation
Transformers
Safetensors
llama
trl
kto
Generated from Trainer
text-generation-inference
Instructions to use stojchet/kto_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stojchet/kto_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="stojchet/kto_test")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("stojchet/kto_test") model = AutoModelForCausalLM.from_pretrained("stojchet/kto_test") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use stojchet/kto_test with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "stojchet/kto_test" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "stojchet/kto_test", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/stojchet/kto_test
- SGLang
How to use stojchet/kto_test 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 "stojchet/kto_test" \ --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": "stojchet/kto_test", "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 "stojchet/kto_test" \ --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": "stojchet/kto_test", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use stojchet/kto_test with Docker Model Runner:
docker model run hf.co/stojchet/kto_test
End of training
Browse files- README.md +14 -9
- generation_config.json +6 -0
README.md
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---
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license: other
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-
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tags:
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- trl
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- kto
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- generated_from_trainer
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base_model: deepseek-ai/deepseek-coder-1.3b-base
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model-index:
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- name: kto_test
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results: []
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate:
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 16
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- total_train_batch_size: 128
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type:
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 1
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### Framework versions
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- Tokenizers 0.19.1
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---
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library_name: transformers
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license: other
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base_model: deepseek-ai/deepseek-coder-1.3b-base
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tags:
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- trl
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- kto
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- generated_from_trainer
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model-index:
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- name: kto_test
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results: []
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.1
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 16
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- total_train_batch_size: 128
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.1
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- lr_scheduler_warmup_steps: 200
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- num_epochs: 1
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- mixed_precision_training: Native AMP
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### Training results
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### Framework versions
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- Transformers 4.45.0
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- Pytorch 2.5.1+cu124
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- Datasets 2.19.2
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- Tokenizers 0.20.3
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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": 32013,
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"eos_token_id": 32014,
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"transformers_version": "4.45.0"
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}
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