Instructions to use Sarim-Hash/Qwen3.5-4B-Winner-SFT-Epochs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sarim-Hash/Qwen3.5-4B-Winner-SFT-Epochs with Transformers:

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

pipe = pipeline("text-generation", model="Sarim-Hash/Qwen3.5-4B-Winner-SFT-Epochs")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Sarim-Hash/Qwen3.5-4B-Winner-SFT-Epochs", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Sarim-Hash/Qwen3.5-4B-Winner-SFT-Epochs with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sarim-Hash/Qwen3.5-4B-Winner-SFT-Epochs"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sarim-Hash/Qwen3.5-4B-Winner-SFT-Epochs",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Sarim-Hash/Qwen3.5-4B-Winner-SFT-Epochs

SGLang

How to use Sarim-Hash/Qwen3.5-4B-Winner-SFT-Epochs 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 "Sarim-Hash/Qwen3.5-4B-Winner-SFT-Epochs" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sarim-Hash/Qwen3.5-4B-Winner-SFT-Epochs",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Sarim-Hash/Qwen3.5-4B-Winner-SFT-Epochs" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sarim-Hash/Qwen3.5-4B-Winner-SFT-Epochs",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Sarim-Hash/Qwen3.5-4B-Winner-SFT-Epochs with Docker Model Runner:
```
docker model run hf.co/Sarim-Hash/Qwen3.5-4B-Winner-SFT-Epochs
```

Qwen3.5-4B Winner-Only Debate SFT

This public repository contains full-parameter checkpoints from three epochs of supervised fine-tuning of Qwen/Qwen3.5-4B on judge-selected winning arguments from multi-round, Wikipedia-grounded debates:

epoch-1-step-315/
epoch-2-step-630/
epoch-3-step-945/

The exact epoch-3 checkpoint used in the eight-model debate evaluation has model.safetensors SHA-256 9160631bf9cbcd66bb3e0b18c074a5a343dadfc6d2d9616d53f80088025ca0c9.

The original rolling-checkpoint run retained epoch 3 but pruned the exact epoch-1 and epoch-2 files. Epochs 1 and 2 were therefore reconstructed from the same data, configuration, seeds, software, and three-GPU setup, saving model weights at each epoch boundary. provenance.json records every weight hash and whether the reproduced epoch-3 checkpoint is byte-identical to the evaluated epoch-3 checkpoint. If it differs, the reproduced epoch-3 weights are also retained under reproduced-run/epoch-3-step-945/.

Training

Training examples: 3,780 conversations from 180 topics
Validation examples: 420 conversations from 20 disjoint topics
Supervision: all four arguments from each judge-selected winning side
Fine-tuning: full language-model weights; not LoRA
Context cutoff: 4,096 tokens
Epochs: 3
Effective batch size: 12
Learning rate: 1e-5
Scheduler: cosine with 5% warmup
Precision: BF16
Distributed training: 3 GPUs with DeepSpeed ZeRO-3
Random seed and data seed: 42
Prompt template: qwen3_5_nothink

Evaluation

The exact epoch-3 model debated seven opponents on the same 200 topics used to derive its training conversations. An identity-blind OpenAI GPT-5.6-Sol evaluator selected one winner per debate.

Overall: 718 wins / 682 losses (51.29%), rank 5 of 8
gemma-e2b: 118 / 200 (59.0%)
gemma-e4b: 116 / 200 (58.0%)
base Qwen3.5-4B: 113 / 200 (56.5%)
Qwen3.5-9B: 97 / 200 (48.5%)
Qwen3.5-27B: 92 / 200 (46.0%)
Qwen3.5-122B-A10B-FP8: 92 / 200 (46.0%)
Qwen3.5-2B: 90 / 200 (45.0%)

This is an in-sample, training-topic evaluation and must not be interpreted as held-out generalization.

Loading a checkpoint

from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "Sarim-Hash/Qwen3.5-4B-Winner-SFT-Epochs"
subfolder = "epoch-3-step-945"

tokenizer = AutoTokenizer.from_pretrained(repo_id, subfolder=subfolder)
model = AutoModelForCausalLM.from_pretrained(
    repo_id,
    subfolder=subfolder,
    torch_dtype="auto",
    device_map="auto",
)

Use the supplied chat template with thinking disabled to match training and evaluation.

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