Instructions to use JayZenith/RLVR_HELDOUT69_PASSK_STEP25 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JayZenith/RLVR_HELDOUT69_PASSK_STEP25 with Transformers:

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

pipe = pipeline("text-generation", model="JayZenith/RLVR_HELDOUT69_PASSK_STEP25")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("JayZenith/RLVR_HELDOUT69_PASSK_STEP25")
model = AutoModelForCausalLM.from_pretrained("JayZenith/RLVR_HELDOUT69_PASSK_STEP25")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use JayZenith/RLVR_HELDOUT69_PASSK_STEP25 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JayZenith/RLVR_HELDOUT69_PASSK_STEP25"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JayZenith/RLVR_HELDOUT69_PASSK_STEP25",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/JayZenith/RLVR_HELDOUT69_PASSK_STEP25

SGLang

How to use JayZenith/RLVR_HELDOUT69_PASSK_STEP25 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 "JayZenith/RLVR_HELDOUT69_PASSK_STEP25" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JayZenith/RLVR_HELDOUT69_PASSK_STEP25",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "JayZenith/RLVR_HELDOUT69_PASSK_STEP25" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JayZenith/RLVR_HELDOUT69_PASSK_STEP25",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use JayZenith/RLVR_HELDOUT69_PASSK_STEP25 with Docker Model Runner:
```
docker model run hf.co/JayZenith/RLVR_HELDOUT69_PASSK_STEP25
```

RLVR_HELDOUT69_PASSK_STEP25

This is the step-25 checkpoint from the targeted RLVR run starting from JayZenith/SFT_V1.

Training target: 8 held-out-failure prompts where SFT_V1 had latent capability under pass@8 (0 < solves < 8).

Matched vLLM pass@8 result on those 8 prompts:

SFT_V1:   47/64 = 0.734
step_25:  54/64 = 0.844
delta:    +7 solves, +10.9 pts

Run artifacts and exact commands are documented in the glyph repo under:

results/RLVR_HELDOUT69_PASSK_STEP25/
README.md
blog/blog_copy_copy.md

Important scope note: this is a narrow RLVR reliability lift on a targeted mixed band, not a claim of broad Rust generalization.

Downloads last month: -

Safetensors

Model size

4B params

Tensor type

BF16

Model tree for JayZenith/RLVR_HELDOUT69_PASSK_STEP25

Base model

JayZenith/SFT_V1

Finetuned

(1)

this model