Instructions to use jihwan1205/svp-v-coconut-gpt2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jihwan1205/svp-v-coconut-gpt2 with Transformers:

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

pipe = pipeline("text-generation", model="jihwan1205/svp-v-coconut-gpt2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jihwan1205/svp-v-coconut-gpt2")
model = AutoModelForCausalLM.from_pretrained("jihwan1205/svp-v-coconut-gpt2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jihwan1205/svp-v-coconut-gpt2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jihwan1205/svp-v-coconut-gpt2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jihwan1205/svp-v-coconut-gpt2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/jihwan1205/svp-v-coconut-gpt2

SGLang

How to use jihwan1205/svp-v-coconut-gpt2 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 "jihwan1205/svp-v-coconut-gpt2" \
    --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": "jihwan1205/svp-v-coconut-gpt2",
		"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 "jihwan1205/svp-v-coconut-gpt2" \
        --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": "jihwan1205/svp-v-coconut-gpt2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jihwan1205/svp-v-coconut-gpt2 with Docker Model Runner:
```
docker model run hf.co/jihwan1205/svp-v-coconut-gpt2
```

jihwan1205 commited on 7 days ago

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Update README.md

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@@ -28,8 +28,8 @@ Only the value-projection weights (the V columns of each `c_attn`) differ from t
 checkpoint. The perturbation is a training-time exploration mechanism and is **not** used
 at deployment: inference is ordinary greedy decoding.
-This repository contains the paper's GPT-2 SVP-V-GRPO checkpoint from epoch 9 of the
-15-epoch `B=32`, `lr=6e-5`, seed-0 run. The results below are the paper's Figure 3
 evaluations; they have not been independently rerun for this model-card update.
 ## Results

 checkpoint. The perturbation is a training-time exploration mechanism and is **not** used
 at deployment: inference is ordinary greedy decoding.
+This repository contains the paper's GPT-2 SVP-V-GRPO checkpoint from epoch 9
+`B=32`, `lr=6e-5`, seed-0 run. The results below are the paper's Figure 3
 evaluations; they have not been independently rerun for this model-card update.
 ## Results