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
```

vhallac commited on Jul 29

Commit

7d79d86

verified ·

1 Parent(s): 2f1d6b1

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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.