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 27 days ago

Commit

a996045

verified ·

1 Parent(s): 0efa1f0

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@@ -11,7 +11,7 @@ tags:
 - grpo
 - reinforcement-learning
 datasets:
-- gsm8k
 ---
 # SVP-V-GRPO · COCONUT GPT-2
@@ -74,7 +74,7 @@ inference code.
 | | |
 |---|---|
 | Base | `ModalityDance/latent-tts-coconut` (COCONUT GPT-2, 124M) |
-| Data | GSM8K-Aug training stream (385k), 9 epochs |
 | Algorithm | GRPO — G=32 rollouts/prompt, B=8 prompts/update, μ=2 inner epochs, DAPO mixed-outcome filter, Dr.GRPO advantage, k3 KL (β=0.02) to the frozen base |
 | Exploration | SVP: per-rollout multiplicative Gaussian jitter on the singular values of `W_V` (σᵢ → σᵢ(1+αgᵢ), α=0.6), re-anchored every 50 iterations |
 | Trained parameters | V columns of every `c_attn` (≈ 7M of 124M) |

 - grpo
 - reinforcement-learning
 datasets:
+- zen-E/GSM8k-Aug
 ---
 # SVP-V-GRPO · COCONUT GPT-2
 | | |
 |---|---|
 | Base | `ModalityDance/latent-tts-coconut` (COCONUT GPT-2, 124M) |
+| Data | [GSM8K-Aug](https://huggingface.co/datasets/zen-E/GSM8k-Aug) training split (385,620 problems), 9 epochs |
 | Algorithm | GRPO — G=32 rollouts/prompt, B=8 prompts/update, μ=2 inner epochs, DAPO mixed-outcome filter, Dr.GRPO advantage, k3 KL (β=0.02) to the frozen base |
 | Exploration | SVP: per-rollout multiplicative Gaussian jitter on the singular values of `W_V` (σᵢ → σᵢ(1+αgᵢ), α=0.6), re-anchored every 50 iterations |
 | Trained parameters | V columns of every `c_attn` (≈ 7M of 124M) |