Instructions to use vhallac/qwen3-0.6b-nope-recal-1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vhallac/qwen3-0.6b-nope-recal-1b with Transformers:

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

pipe = pipeline("text-generation", model="vhallac/qwen3-0.6b-nope-recal-1b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vhallac/qwen3-0.6b-nope-recal-1b")
model = AutoModelForCausalLM.from_pretrained("vhallac/qwen3-0.6b-nope-recal-1b", device_map="auto")
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 vhallac/qwen3-0.6b-nope-recal-1b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vhallac/qwen3-0.6b-nope-recal-1b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vhallac/qwen3-0.6b-nope-recal-1b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vhallac/qwen3-0.6b-nope-recal-1b

SGLang

How to use vhallac/qwen3-0.6b-nope-recal-1b 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 "vhallac/qwen3-0.6b-nope-recal-1b" \
    --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": "vhallac/qwen3-0.6b-nope-recal-1b",
		"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 "vhallac/qwen3-0.6b-nope-recal-1b" \
        --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": "vhallac/qwen3-0.6b-nope-recal-1b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use vhallac/qwen3-0.6b-nope-recal-1b with Docker Model Runner:
```
docker model run hf.co/vhallac/qwen3-0.6b-nope-recal-1b
```

vhallac commited on Aug 9

Commit

f8f6eb9

verified ·

1 Parent(s): 3a46e2b

Add resumable training checkpoint (ckpt.pt)

Browse files

Resumable checkpoint from NoPE/DroPE training run. Includes weights + AdamW optimizer state + scheduler state + RNG states.

Files changed (1) hide show

training_checkpoint/ckpt.pt +3 -0

training_checkpoint/ckpt.pt ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:a8bc4b33470065ae0528f0d170148d6b2ff49b5b058948e5eb3a82978c0fb51d
+size 3576700669