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

Libraries

How to use vhallac/qwen3-0.6b-rope-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-rope-recal-1b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vhallac/qwen3-0.6b-rope-recal-1b")
model = AutoModelForCausalLM.from_pretrained("vhallac/qwen3-0.6b-rope-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=256)
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-rope-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-rope-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-rope-recal-1b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

Qwen3-0.6B — RoPE retained, recalibrated on 1B tokens (control model)

A research artifact, not a general-purpose model. This is Qwen3-0.6B recalibrated on 1B tokens of FineWeb-Edu with RoPE left active throughout. It is the matched control for vhallac/qwen3-0.6b-nope-recal-1b, which received identical training — same corpus, same cached token stream, same step count, same schedule, same seed — with RoPE removed.

The only difference between the two models is RoPE. That is the entire point of this checkpoint.

Loading is unremarkable — this is a standard Qwen3 model:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "vhallac/qwen3-0.6b-rope-recal-1b"
model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained(model_id)

(Its sibling does require a runtime patch. Read that model's card before using it.)

Why a control model exists

Part of the rope-as-scaffold research program, which tested whether RoPE is a training scaffold that can be discarded after pretraining.

The problem this checkpoint solves: earlier comparisons pitted a RoPE-removed model that had received 1B extra tokens against an untouched base model that had received none. Any difference confounds "RoPE was removed" with "received extra in-domain training." Since the recalibration corpus is also the evaluation domain, that confound is large and it runs in a direction that flatters the RoPE-removed model.

This model holds the training constant so the comparison isolates RoPE. It changed the program's conclusions: one prior result reversed sign, another was halved in magnitude, and a persistent perplexity penalty became visible that had been invisible against the un-adapted baseline.

Results

Held-out FineWeb-Edu perplexity, fp32, 5M-token frozen eval slice, identical harness for all three:

model	CE	PPL
`Qwen/Qwen3-0.6B` (base, no extra training)	3.0819	21.80
this model (control, RoPE on)	2.6569	14.25
`qwen3-0.6b-nope-recal-1b` (RoPE removed)	2.8260	16.88

Reading these correctly matters. Recalibration is worth 0.425 nats (3.0819 → 2.6569). Removing RoPE gives 0.169 nats of that back (2.6569 → 2.8260). The RoPE-removed model's apparent improvement over base (21.80 → 16.88) is the net of those two, and reporting it against base alone makes a real regression look like a gain.

Known limitations

Effective context ≈ 2048 tokens for the recalibrated behaviour, despite max_position_embeddings: 40960 inherited from base. Unlike its NoPE sibling this model does not collapse beyond 2048 (it inherits base's 32k-context pretraining), but its recalibration only covered 2048.
English-only recalibration corpus (FineWeb-Edu), while Qwen3 is multilingual + code + math. Expect domain skew relative to base outside the eval domain.
Not instruction-tuned beyond whatever base carried; recalibration was plain LM training.
Single seed, single recipe, 0.6B scale.

Training


base	`Qwen/Qwen3-0.6B` @ `c1899de289a04d12100db370d81485cdf75e47ca`
rotary	active throughout (no patch applied)
corpus	`HuggingFaceFW/fineweb-edu`, `sample-10BT`, streamed in provider order
tokens	1B (1907 steps × 524,288 tokens), context 2048
optimizer	AdamW β=(0.9, 0.95), wd 0.1, grad clip 1.0
LR	1e-3 peak, 2% warmup, cosine → 10% of peak
precision	bf16
seed	0
hardware	1× H100 SXM, ~7h

Trained from the same cached token file as its NoPE sibling, not merely the same corpus — the two models saw the identical token sequence in the identical order. training_metrics.csv and training_manifest.json are included for full provenance.

Note: training_manifest.json in this repo carries "artifact": "qwen3-droped" — a known mislabel from a hardcoded string in the training script. Every functional field is correct for this model, including rotary_patch_applied: false and output_dir: /workspace/qwen3-rope-recal.

Reproduction and full analysis

Code, specs, and lab notebook: vhallac/crockpot-experiments
The controlled comparison this model enabled: NOTEBOOK.md, entry 2026-07-28 — RS-amendment-2-3
Pre-registered plan: RS-amendment-2-3.md; recipe of record RS1-spec.md §11

License

Apache 2.0, inherited from Qwen3-0.6B.

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Safetensors

Model size

0.6B params

Tensor type

BF16

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