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

Libraries

How to use Ba2han/experimental_auto with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Ba2han/experimental_auto", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Ba2han/experimental_auto", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Ba2han/experimental_auto", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Ba2han/experimental_auto with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ba2han/experimental_auto"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ba2han/experimental_auto",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Ba2han/experimental_auto

SGLang

How to use Ba2han/experimental_auto 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/experimental_auto" \
    --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/experimental_auto",
		"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/experimental_auto" \
        --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/experimental_auto",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio

How to use Ba2han/experimental_auto 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/experimental_auto 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/experimental_auto to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Ba2han/experimental_auto to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Ba2han/experimental_auto",
    max_seq_length=2048,
)

Docker Model Runner
How to use Ba2han/experimental_auto with Docker Model Runner:
```
docker model run hf.co/Ba2han/experimental_auto
```

Ba2han commited on 18 days ago

Commit

e3b6b8c

verified ·

1 Parent(s): c49037d

Training in progress, step 50

Browse files

Files changed (3) hide show

README.md +2 -2
model.safetensors +1 -1
training_args.bin +1 -1

README.md CHANGED Viewed

@@ -3,8 +3,8 @@ library_name: transformers
 model_name: experimental_auto
 tags:
 - generated_from_trainer
-- unsloth
 - trl
 - sft
 licence: license
 ---
@@ -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/58s5ohid)
 This model was trained with SFT.

 model_name: experimental_auto
 tags:
 - generated_from_trainer
 - trl
+- unsloth
 - sft
 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/3icdczzn)
 This model was trained with SFT.

model.safetensors CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:61b4ce55f856a67bfdbd003ebe9b33428ea067be73223fd9c3bf1c9940e96dfe
 size 1008303016

 version https://git-lfs.github.com/spec/v1
+oid sha256:df7f3da2bd850f33f7298372ff869be8f81c2b3405227fe6c9bd7c6f2f71a131
 size 1008303016

training_args.bin CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:25d686833094b637e69f7be1bd7dd0c173bb768a2a8b1916830852d7703b16ed
 size 5713

 version https://git-lfs.github.com/spec/v1
+oid sha256:9224b9e115d57261205215ceae02ffa4a6e8577fbe762a2cbd2cd5c8a9f1ea82
 size 5713