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 9 days ago

Commit

3d8d460

verified ·

1 Parent(s): c990511

Training in progress, step 572

Browse files

Files changed (5) hide show

README.md +2 -2
config.json +7 -5
generation_config.json +1 -1
model.safetensors +2 -2
training_args.bin +1 -1

README.md CHANGED Viewed

@@ -27,7 +27,7 @@ print(output["generated_text"])
 ## 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.
@@ -37,7 +37,7 @@ This model was trained with SFT.
 - TRL: 0.24.0
 - Transformers: 5.5.0
 - Pytorch: 2.10.0
-- Datasets: 4.3.0
 - Tokenizers: 0.22.2
 ## Citations

 ## 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/dihq285b)
 This model was trained with SFT.
 - TRL: 0.24.0
 - Transformers: 5.5.0
 - Pytorch: 2.10.0
+- Datasets: 4.6.0
 - Tokenizers: 0.22.2
 ## Citations

config.json CHANGED Viewed

@@ -9,9 +9,9 @@
   "eos_token_id": 3,
   "head_dim": 128,
   "hidden_act": "silu",
-  "hidden_size": 1536,
   "initializer_range": 0.02,
-  "intermediate_size": 4608,
   "layer_types": [
     "full_attention",
     "full_attention",
@@ -50,14 +50,16 @@
     "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,
@@ -68,7 +70,7 @@
   "sliding_window": null,
   "tie_word_embeddings": true,
   "transformers_version": "5.5.0",
-  "unsloth_version": "2026.6.9",
   "use_cache": false,
   "use_sliding_window": false,
   "vocab_size": 60800

   "eos_token_id": 3,
   "head_dim": 128,
   "hidden_act": "silu",
+  "hidden_size": 1280,
   "initializer_range": 0.02,
+  "intermediate_size": 4480,
   "layer_types": [
     "full_attention",
     "full_attention",
     "full_attention",
     "full_attention",
     "full_attention",
+    "full_attention",
+    "full_attention",
     "full_attention"
   ],
   "max_position_embeddings": 8192,
+  "max_window_layers": 40,
   "model_name": "random_model",
   "model_type": "qwen3",
   "num_attention_heads": 16,
+  "num_hidden_layers": 40,
   "num_key_value_heads": 8,
   "pad_token_id": 0,
   "rms_norm_eps": 1e-08,
   "sliding_window": null,
   "tie_word_embeddings": true,
   "transformers_version": "5.5.0",
+  "unsloth_version": "2026.7.1",
   "use_cache": false,
   "use_sliding_window": false,
   "vocab_size": 60800

generation_config.json CHANGED Viewed

@@ -9,5 +9,5 @@
   "output_hidden_states": false,
   "pad_token_id": 0,
   "transformers_version": "5.5.0",
-  "use_cache": true
 }

   "output_hidden_states": false,
   "pad_token_id": 0,
   "transformers_version": "5.5.0",
+  "use_cache": false
 }

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