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
```

qwen3-0.6b-nope-recal-1b / training_manifest.json

vhallac

Add training_manifest.json

a57b83f verified 2 months ago

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	{
	"artifact": "qwen3-droped",
	"config": {
	"adam_beta1": 0.9,
	"adam_beta2": 0.95,
	"adam_eps": 1e-08,
	"base_model": "Qwen/Qwen3-0.6B",
	"base_revision": "c1899de289a04d12100db370d81485cdf75e47ca",
	"dataset_name": "sample-10BT",
	"dataset_path": "HuggingFaceFW/fineweb-edu",
	"dataset_split": "train",
	"dtype": "bf16",
	"eval_slice_rule": "stream HuggingFaceFW/fineweb-edu sample-10BT train in provider order; concatenate non-empty document text with one EOS token after each document; use the first eval_tokens tokens as the held-out eval slice; training starts immediately after that prefix",
	"eval_tokens": 5000000,
	"global_batch_tokens": 524288,
	"grad_clip": 1.0,
	"learning_rate": 0.001,
	"micro_batch_size": 8,
	"min_lr_fraction": 0.1,
	"seed": 0,
	"train_context": 2048,
	"train_tokens": 1000000000,
	"warmup_fraction": 0.02,
	"weight_decay": 0.1
	},
	"created_at": "2026-07-25T17:26:13Z",
	"grad_accumulation_steps": 32,
	"hardware": {
	"cuda_name": "NVIDIA H100 80GB HBM3",
	"device": "cuda"
	},
	"output_dir": "/workspace/qwen3-droped",
	"rotary_identity_probe": {
	"identity_cos_max_abs_error": 0.0,
	"identity_sin_max_abs_error": 0.0,
	"pass": true,
	"true_rope_max_abs_delta_from_identity": 2.0
	},
	"token_cache": {
	"eval_path": "/workspace/rs1b-token-cache/fineweb_edu_qwen3_eval_5000000.uint32",
	"train_path": "/workspace/rs1b-token-cache/fineweb_edu_qwen3_train_after_eval5000000_1000000000.uint32"
	},
	"total_steps": 1907
	}