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") - 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
Update training progress and artifacts
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README.md
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- **Diverse Datasets:** The script iterates through datasets available on the Hugging Face Hub, attempting to train on each.
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- **Short Training Iterations:** Each training run per dataset configuration is currently set to a small number of steps (`max_steps=1`) to allow for rapid iteration across many datasets.
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## Training Progress
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- **Datasets Processed (Successfully trained on at least one config):**
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- **Text Examples Streamed (Total):**
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- **Tokens Processed (Total):**
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- **Last Successful Model Update:** 2025-05-07 14:
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### Evaluation Metrics
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- **Overall Perplexity (on a small fixed dataset):**
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#### Generated Examples (Qualitative Assessment)
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- **Diverse Datasets:** The script iterates through datasets available on the Hugging Face Hub, attempting to train on each.
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- **Short Training Iterations:** Each training run per dataset configuration is currently set to a small number of steps (`max_steps=1`) to allow for rapid iteration across many datasets.
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## Training Progress
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- **Datasets Processed (Successfully trained on at least one config):** 9
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- **Text Examples Streamed (Total):** 54
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- **Tokens Processed (Total):** 27648
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- **Last Successful Model Update:** 2025-05-07 14:28:37 UTC
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### Evaluation Metrics
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- **Overall Perplexity (on a small fixed dataset):** 143030.67
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#### Generated Examples (Qualitative Assessment)
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model.safetensors
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