Text Generation
Transformers
Safetensors
gpt2
latent-reasoning
continuous-thought
coconut
grpo
reinforcement-learning
text-generation-inference
Instructions to use jihwan1205/svp-v-coconut-gpt2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Update README.md
Browse files
README.md
CHANGED
|
@@ -20,15 +20,13 @@ A [COCONUT](https://huggingface.co/ModalityDance/latent-tts-coconut) GPT-2 (124M
|
|
| 20 |
latent-reasoning model post-trained with **SVP-V-GRPO** — reinforcement learning whose
|
| 21 |
rollout exploration comes from *weight-space* perturbation of the attention value
|
| 22 |
projections. For each layer and rollout, SVP independently samples Gaussian noise and
|
| 23 |
-
rescales coefficients in the fixed SVD basis of `W_V` as `sᵢ → sᵢ(1+αgᵢ)`.
|
| 24 |
-
coefficients can be negative and are not necessarily singular values of the perturbed
|
| 25 |
-
matrix.
|
| 26 |
|
| 27 |
Only the value-projection weights (the V columns of each `c_attn`) differ from the base
|
| 28 |
checkpoint. The perturbation is a training-time exploration mechanism and is **not** used
|
| 29 |
at deployment: inference is ordinary greedy decoding.
|
| 30 |
|
| 31 |
-
This repository contains the paper's GPT-2 SVP-V-GRPO checkpoint from epoch 9 `B=32`, `lr=6e-5`
|
| 32 |
|
| 33 |
## Results
|
| 34 |
|
|
|
|
| 20 |
latent-reasoning model post-trained with **SVP-V-GRPO** — reinforcement learning whose
|
| 21 |
rollout exploration comes from *weight-space* perturbation of the attention value
|
| 22 |
projections. For each layer and rollout, SVP independently samples Gaussian noise and
|
| 23 |
+
rescales coefficients in the fixed SVD basis of `W_V` as `sᵢ → sᵢ(1+αgᵢ)`.
|
|
|
|
|
|
|
| 24 |
|
| 25 |
Only the value-projection weights (the V columns of each `c_attn`) differ from the base
|
| 26 |
checkpoint. The perturbation is a training-time exploration mechanism and is **not** used
|
| 27 |
at deployment: inference is ordinary greedy decoding.
|
| 28 |
|
| 29 |
+
This repository contains the paper's GPT-2 SVP-V-GRPO checkpoint from epoch 9 `B=32`, `lr=6e-5` run.
|
| 30 |
|
| 31 |
## Results
|
| 32 |
|