Text Generation
Transformers
Safetensors
qwen3
Generated from Trainer
unsloth
trl
sft
text-generation-inference
Instructions to use Ba2han/outputs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ba2han/outputs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Ba2han/outputs")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Ba2han/outputs") model = AutoModelForCausalLM.from_pretrained("Ba2han/outputs") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Ba2han/outputs with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Ba2han/outputs" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Ba2han/outputs", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Ba2han/outputs
- SGLang
How to use Ba2han/outputs 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/outputs" \ --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/outputs", "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/outputs" \ --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/outputs", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Unsloth Studio
How to use Ba2han/outputs 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/outputs 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/outputs to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Ba2han/outputs to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Ba2han/outputs", max_seq_length=2048, ) - Docker Model Runner
How to use Ba2han/outputs with Docker Model Runner:
docker model run hf.co/Ba2han/outputs
Training in progress, step 660
Browse files- README.md +3 -3
- config.json +12 -10
- generation_config.json +2 -2
- model.safetensors +2 -2
- tokenizer.json +0 -18
- tokenizer_config.json +1 -20
- training_args.bin +1 -1
README.md
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model_name: outputs
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tags:
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- generated_from_trainer
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- sft
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- unsloth
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- trl
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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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- TRL: 0.24.0
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- Transformers: 5.5.0
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- Pytorch: 2.10.0
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- Datasets: 4.
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- Tokenizers: 0.22.2
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## Citations
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model_name: outputs
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tags:
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- generated_from_trainer
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- unsloth
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- trl
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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/g2ae0u89)
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This model was trained with SFT.
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- TRL: 0.24.0
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- Transformers: 5.5.0
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- Pytorch: 2.10.0
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+
- Datasets: 4.7.0
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- Tokenizers: 0.22.2
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## Citations
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config.json
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"attention_dropout": 0.0,
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"bos_token_id": 2,
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"dtype": "bfloat16",
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-
"eos_token_id":
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size":
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"initializer_range": 0.02,
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"intermediate_size":
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings":
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"max_window_layers":
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"model_name": "
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"model_type": "qwen3",
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"num_attention_heads":
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"num_hidden_layers":
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"num_key_value_heads":
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"pad_token_id": 0,
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"rms_norm_eps": 1e-08,
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"rope_parameters": {
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"unsloth_version": "2026.7.2",
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"use_cache": false,
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"use_sliding_window": false,
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"vocab_size":
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}
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"attention_dropout": 0.0,
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"bos_token_id": 2,
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"dtype": "bfloat16",
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"eos_token_id": 3,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 1664,
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"initializer_range": 0.02,
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"intermediate_size": 4160,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 16384,
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"max_window_layers": 42,
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"model_name": "random_model",
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"model_type": "qwen3",
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"num_attention_heads": 12,
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"num_hidden_layers": 42,
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"num_key_value_heads": 4,
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"pad_token_id": 0,
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"rms_norm_eps": 1e-08,
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"rope_parameters": {
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"unsloth_version": "2026.7.2",
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"use_cache": false,
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"use_sliding_window": false,
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"vocab_size": 60800
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}
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generation_config.json
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"_from_model_config": true,
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"bos_token_id": 2,
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"eos_token_id": [
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],
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"max_length":
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"output_attentions": false,
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"output_hidden_states": false,
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"pad_token_id": 0,
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"_from_model_config": true,
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"bos_token_id": 2,
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"eos_token_id": [
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"max_length": 16384,
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"output_attentions": false,
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"output_hidden_states": false,
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"pad_token_id": 0,
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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size
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size 2519929824
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tokenizer.json
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"rstrip": false,
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"special": true
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{
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"normalizer": {
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"special": true
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"normalizer": {
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tokenizer_config.json
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"backend": "tokenizers",
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"bos_token": "<bos>",
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"clean_up_tokenization_spaces": false,
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"eos_token": "<
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"extra_special_tokens": [
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"<|im_start|>"
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],
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"is_local": true,
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "<pad>",
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"rstrip": false,
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"normalized": false,
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"special": true
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"backend": "tokenizers",
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"bos_token": "<bos>",
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"clean_up_tokenization_spaces": false,
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"eos_token": "<eos>",
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"is_local": true,
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "<pad>",
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"rstrip": false,
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training_args.bin
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