Instructions to use JocelyneSmith/HW2-supervised with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JocelyneSmith/HW2-supervised with Transformers:

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

pipe = pipeline("text-generation", model="JocelyneSmith/HW2-supervised")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("JocelyneSmith/HW2-supervised")
model = AutoModelForCausalLM.from_pretrained("JocelyneSmith/HW2-supervised")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use JocelyneSmith/HW2-supervised with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JocelyneSmith/HW2-supervised"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JocelyneSmith/HW2-supervised",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/JocelyneSmith/HW2-supervised

SGLang

How to use JocelyneSmith/HW2-supervised 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 "JocelyneSmith/HW2-supervised" \
    --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": "JocelyneSmith/HW2-supervised",
		"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 "JocelyneSmith/HW2-supervised" \
        --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": "JocelyneSmith/HW2-supervised",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use JocelyneSmith/HW2-supervised with Docker Model Runner:
```
docker model run hf.co/JocelyneSmith/HW2-supervised
```

JocelyneSmith commited on Jun 30, 2025

Commit

06a0b21

verified ·

1 Parent(s): 613f23f

Training in progress, step 500

Browse files

Files changed (4) hide show

README.md +3 -3
config.json +1 -1
model.safetensors +1 -1
training_args.bin +1 -1

README.md CHANGED Viewed

@@ -4,8 +4,8 @@ library_name: transformers
 model_name: HW2-supervised
 tags:
 - generated_from_trainer
-- trl
 - sft
 licence: license
 ---
@@ -35,10 +35,10 @@ This model was trained with SFT.
 ### Framework versions
 - TRL: 0.19.0
-- Transformers: 4.52.4
 - Pytorch: 2.7.1+cu128
 - Datasets: 3.6.0
-- Tokenizers: 0.21.1
 ## Citations

 model_name: HW2-supervised
 tags:
 - generated_from_trainer
 - sft
+- trl
 licence: license
 ---
 ### Framework versions
 - TRL: 0.19.0
+- Transformers: 4.53.0
 - Pytorch: 2.7.1+cu128
 - Datasets: 3.6.0
+- Tokenizers: 0.21.2
 ## Citations

config.json CHANGED Viewed

@@ -33,7 +33,7 @@
     }
   },
   "torch_dtype": "float32",
-  "transformers_version": "4.52.4",
   "use_cache": true,
   "vocab_size": 50257
 }

     }
   },
   "torch_dtype": "float32",
+  "transformers_version": "4.53.0",
   "use_cache": true,
   "vocab_size": 50257
 }

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