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-recal-1b / README.md

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	---
	license: apache-2.0
	base_model: Qwen/Qwen3-0.6B
	datasets:
	- HuggingFaceFW/fineweb-edu
	language:
	- en
	tags:
	- qwen3
	- positional-encoding
	- interpretability
	- control-model
	- research
	library_name: transformers
	---

	# 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`](https://huggingface.co/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:

	```python
	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`](https://github.com/vhallac/crockpot-experiments/tree/main/experiments/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](https://github.com/vhallac/crockpot-experiments/tree/main/experiments/rope-as-scaffold)
	- 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.