Instructions to use MooreMuaMu/qwen35-27b-ancient-rl-r32-step100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MooreMuaMu/qwen35-27b-ancient-rl-r32-step100 with Transformers:

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

pipe = pipeline("text-generation", model="MooreMuaMu/qwen35-27b-ancient-rl-r32-step100")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("MooreMuaMu/qwen35-27b-ancient-rl-r32-step100")
model = AutoModelForMultimodalLM.from_pretrained("MooreMuaMu/qwen35-27b-ancient-rl-r32-step100", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MooreMuaMu/qwen35-27b-ancient-rl-r32-step100 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MooreMuaMu/qwen35-27b-ancient-rl-r32-step100"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MooreMuaMu/qwen35-27b-ancient-rl-r32-step100",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MooreMuaMu/qwen35-27b-ancient-rl-r32-step100

SGLang

How to use MooreMuaMu/qwen35-27b-ancient-rl-r32-step100 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 "MooreMuaMu/qwen35-27b-ancient-rl-r32-step100" \
    --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": "MooreMuaMu/qwen35-27b-ancient-rl-r32-step100",
		"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 "MooreMuaMu/qwen35-27b-ancient-rl-r32-step100" \
        --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": "MooreMuaMu/qwen35-27b-ancient-rl-r32-step100",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MooreMuaMu/qwen35-27b-ancient-rl-r32-step100 with Docker Model Runner:
```
docker model run hf.co/MooreMuaMu/qwen35-27b-ancient-rl-r32-step100
```

qwen35-27b-ancient-rl-r32-step100

This repository contains the full merged bfloat16 model for the rank-32 GRPO checkpoint at step 100. It is not an adapter-only upload: the LoRA adapter has been merged into the Qwen3.5-27B ancient-language stage-2 base model, and the repository includes the safetensors shards plus tokenizer/processor/chat-template files needed for direct transformers loading.

Adapter-only sibling: MooreMuaMu/qwen35-27b-ancient-rl-r32-step100-lora.

Intended Use

from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "MooreMuaMu/qwen35-27b-ancient-rl-r32-step100"
tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo_id,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True,
)

Training Source

Base model: /blob/models/qwen35-27b-ancient-stage2/checkpoint-227-merged
LoRA checkpoint: /blob/experiments/ancient-swift-rl-stage2/qwen35_27b_ckpt227_uy_mn_bo_ta_full_r32_lr1e4/v0-20260811-070520/checkpoint-100
LoRA rank/alpha: 32/32
Merge dtype: bfloat16
Serialization: safetensors, 5GB max shard size

Evaluation Summary

Evaluation used the independent six-bucket held-out subset: 300 prompts, 50 per bucket, thinking enabled, temperature 0.7, top-p 0.95, n=1, seed 42. Deltas are prompt-paired against the same base-model generations where applicable. Bootstrap confidence intervals use 10,000 paired samples.

Overall semantic metrics:

Metric	Base	Step 100	Delta	95% CI
Exact	0.3133	0.3400	+0.0267	[-0.0167, +0.0733]
Char-F1	0.5499	0.5579	+0.0080	[-0.0328, +0.0495]
BERTScore-F1	0.7638	0.7657	+0.0019	[-0.0309, +0.0348]
Semantic composite	0.5915	0.6015	+0.0100	[-0.0260, +0.0473]
Has answer	0.9533	0.9467	-0.0067	[-0.0400, +0.0267]

SacreBLEU2 answer-extraction metrics use sacrebleu.metrics.BLEU(tokenize="zh", max_ngram_order=2, smooth_method="exp", effective_order=True) after extracting text inside <ANS>...</ANS> and discarding any prefix before </think>.

Metric	Base	Step 100	Delta
Corpus BLEU-2 (%)	34.30	39.86	+5.56 pp
Sentence BLEU-2 mean (%)	46.12	48.10	+1.98 pp
Nonempty extracted answer	95.0%	94.7%	-0.3 pp

Bucket-level SacreBLEU2:

Bucket	Corpus BLEU-2 base -> step100	Delta	Sentence BLEU-2 base -> step100	Delta	Step100 nonempty
Tibetan annotation	59.14 -> 56.73	-2.41 pp	65.40 -> 66.82	+1.42 pp	94.0%
Tibetan translation	15.68 -> 18.15	+2.47 pp	16.43 -> 16.00	-0.43 pp	94.0%
Traditional Mongolian annotation	53.65 -> 70.42	+16.77 pp	50.84 -> 56.84	+6.01 pp	92.0%
Traditional Mongolian translation	25.52 -> 26.15	+0.63 pp	24.41 -> 29.26	+4.85 pp	100.0%
Uyghur annotation	85.13 -> 90.75	+5.62 pp	87.89 -> 87.26	-0.63 pp	88.0%
Uyghur translation	34.55 -> 36.67	+2.12 pp	31.77 -> 32.41	+0.64 pp	100.0%

Decision note: step 100 is an exploratory checkpoint. Its aggregate semantic improvement is small and the confidence interval crosses zero, although SacreBLEU2 corpus score improves, mainly from Traditional Mongolian annotation.

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Safetensors

Model size

27B params

Tensor type

BF16