Text Generation
Transformers
TensorBoard
Safetensors
qwen3
Generated from Trainer
sft
unsloth
trl
text-generation-inference
Instructions to use Ba2han/test-model-3006 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ba2han/test-model-3006 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Ba2han/test-model-3006")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Ba2han/test-model-3006") model = AutoModelForCausalLM.from_pretrained("Ba2han/test-model-3006") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Ba2han/test-model-3006 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Ba2han/test-model-3006" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Ba2han/test-model-3006", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Ba2han/test-model-3006
- SGLang
How to use Ba2han/test-model-3006 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 "Ba2han/test-model-3006" \ --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": "Ba2han/test-model-3006", "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 "Ba2han/test-model-3006" \ --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": "Ba2han/test-model-3006", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Unsloth Studio
How to use Ba2han/test-model-3006 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Ba2han/test-model-3006 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Ba2han/test-model-3006 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Ba2han/test-model-3006 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Ba2han/test-model-3006", max_seq_length=2048, ) - Docker Model Runner
How to use Ba2han/test-model-3006 with Docker Model Runner:
docker model run hf.co/Ba2han/test-model-3006
Training in progress, step 2000
Browse files
README.md
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model_name: test-model-3006
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tags:
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- generated_from_trainer
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licence: license
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---
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## Training procedure
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/batuhan409/huggingface/runs/
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This model was trained with SFT.
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model_name: test-model-3006
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tags:
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- generated_from_trainer
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- sft
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licence: license
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---
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## Training procedure
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/batuhan409/huggingface/runs/3qtwnodm)
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This model was trained with SFT.
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config.json
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"tie_word_embeddings": true,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.53.0",
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"unsloth_version": "2025.6.
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"use_cache": false,
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"use_sliding_window": false,
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"vocab_size": 151936
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"tie_word_embeddings": true,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.53.0",
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"unsloth_version": "2025.6.11",
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"use_cache": false,
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"use_sliding_window": false,
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"vocab_size": 151936
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model.safetensors
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runs/Jul01_06-48-59_0b75c24f7cf8/events.out.tfevents.1751352543.0b75c24f7cf8.1455.0
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