Text Generation
Transformers
Safetensors
llama
sequential-fine-tuning
lora
text-generation-inference
Instructions to use jnjj/Vvbvv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jnjj/Vvbvv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jnjj/Vvbvv")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("jnjj/Vvbvv") model = AutoModelForCausalLM.from_pretrained("jnjj/Vvbvv", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use jnjj/Vvbvv with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jnjj/Vvbvv" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jnjj/Vvbvv", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jnjj/Vvbvv
- SGLang
How to use jnjj/Vvbvv 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 "jnjj/Vvbvv" \ --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": "jnjj/Vvbvv", "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 "jnjj/Vvbvv" \ --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": "jnjj/Vvbvv", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use jnjj/Vvbvv with Docker Model Runner:
docker model run hf.co/jnjj/Vvbvv
Periodic upload
Browse files- README.md +4 -4
- model.safetensors +1 -1
- training.log +14 -0
README.md
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## Progreso de Entrenamiento
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- **Datasets procesados:**
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- **Ejemplos de texto procesados:**
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- **Tokens procesados:**
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- **Última subida:** 2025-05-06 14:
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## Progreso de Entrenamiento
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- **Datasets procesados:** 40.0
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- **Ejemplos de texto procesados:** 120.0
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- **Tokens procesados:** 37085.0
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- **Última subida:** 2025-05-06 14:44:14 UTC
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training.log
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2025-05-06 16:42:23,851 INFO: Finished training and saved model/tokenizer for reasonir/reasonir-data config hq
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2025-05-06 16:42:23,852 INFO: Starting model update for SWE-bench/SWE-smith, config: default
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2025-05-06 16:42:23,851 INFO: Finished training and saved model/tokenizer for reasonir/reasonir-data config hq
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2025-05-06 16:42:23,852 INFO: Starting model update for SWE-bench/SWE-smith, config: default
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2025-05-06 16:42:27,711 INFO: Finished training and saved model/tokenizer for SWE-bench/SWE-smith config default
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2025-05-06 16:43:14,405 INFO: Upload successful.
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2025-05-06 16:43:16,748 INFO: Preparing data for ZennyKenny/cosa-benchmark-dataset, config: default
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2025-05-06 16:43:16,887 INFO: Preparing data for kindred-soul-ltd/kindred-ecommerce-merchant-deals-dataset, config: default
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2025-05-06 16:43:18,940 INFO: Starting model update for kindred-soul-ltd/kindred-ecommerce-merchant-deals-dataset, config: default
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2025-05-06 16:43:20,769 INFO: Finished training and saved model/tokenizer for kindred-soul-ltd/kindred-ecommerce-merchant-deals-dataset config default
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2025-05-06 16:43:20,770 INFO: Starting model update for ZennyKenny/cosa-benchmark-dataset, config: default
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2025-05-06 16:43:20,770 ERROR: Error in background_training_loop task scheduling: local variable 'merged_model' referenced before assignment
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2025-05-06 16:43:22,496 INFO: Preparing data for deepmind/aqua_rat, config: raw
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