Instructions to use Ba2han/outputs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

Ba2han commited on 11 days ago

Commit

b5113ba

verified ·

1 Parent(s): 7fa1472

Training in progress, step 572

Browse files

Files changed (5) hide show

README.md +2 -2
config.json +5 -11
model.safetensors +2 -2
tokenizer.json +0 -0
training_args.bin +1 -1

README.md CHANGED Viewed

@@ -3,8 +3,8 @@ library_name: transformers
 model_name: outputs
 tags:
 - generated_from_trainer
-- trl
 - sft
 - unsloth
 licence: license
 ---
@@ -27,7 +27,7 @@ print(output["generated_text"])
 ## Training procedure
 This model was trained with SFT.

 model_name: outputs
 tags:
 - generated_from_trainer
 - sft
+- trl
 - unsloth
 licence: license
 ---
 ## Training procedure
+[<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/57lcbf3q)
 This model was trained with SFT.

config.json CHANGED Viewed

@@ -50,20 +50,14 @@
     "full_attention",
     "full_attention",
     "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
-    "full_attention",
     "full_attention"
   ],
   "max_position_embeddings": 8192,
-  "max_window_layers": 44,
-  "model_name": "random_model2",
   "model_type": "qwen3",
   "num_attention_heads": 16,
-  "num_hidden_layers": 44,
   "num_key_value_heads": 8,
   "pad_token_id": 0,
   "rms_norm_eps": 1e-08,
@@ -75,7 +69,7 @@
   "tie_word_embeddings": true,
   "transformers_version": "5.5.0",
   "unsloth_version": "2026.6.9",
-  "use_cache": true,
   "use_sliding_window": false,
-  "vocab_size": 51200
 }

     "full_attention",
     "full_attention",
     "full_attention",
     "full_attention"
   ],
   "max_position_embeddings": 8192,
+  "max_window_layers": 38,
+  "model_name": "random_model",
   "model_type": "qwen3",
   "num_attention_heads": 16,
+  "num_hidden_layers": 38,
   "num_key_value_heads": 8,
   "pad_token_id": 0,
   "rms_norm_eps": 1e-08,
   "tie_word_embeddings": true,
   "transformers_version": "5.5.0",
   "unsloth_version": "2026.6.9",
+  "use_cache": false,
   "use_sliding_window": false,
+  "vocab_size": 60800
 }

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