Instructions to use nmuendler/DeepSeek-R1-Distill-Qwen-7B-rust-early-stop-run1-kl0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nmuendler/DeepSeek-R1-Distill-Qwen-7B-rust-early-stop-run1-kl0 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("/iopsstor/scratch/cscs/nmuendler/hf-cache/hub/models--deepseek-ai--DeepSeek-R1-Distill-Qwen-7B/snapshots/916b56a44061fd5cd7d6a8fb632557ed4f724f60")
model = PeftModel.from_pretrained(base_model, "nmuendler/DeepSeek-R1-Distill-Qwen-7B-rust-early-stop-run1-kl0")

Transformers

How to use nmuendler/DeepSeek-R1-Distill-Qwen-7B-rust-early-stop-run1-kl0 with Transformers:

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

pipe = pipeline("text-generation", model="nmuendler/DeepSeek-R1-Distill-Qwen-7B-rust-early-stop-run1-kl0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("nmuendler/DeepSeek-R1-Distill-Qwen-7B-rust-early-stop-run1-kl0", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nmuendler/DeepSeek-R1-Distill-Qwen-7B-rust-early-stop-run1-kl0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nmuendler/DeepSeek-R1-Distill-Qwen-7B-rust-early-stop-run1-kl0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nmuendler/DeepSeek-R1-Distill-Qwen-7B-rust-early-stop-run1-kl0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nmuendler/DeepSeek-R1-Distill-Qwen-7B-rust-early-stop-run1-kl0

SGLang

How to use nmuendler/DeepSeek-R1-Distill-Qwen-7B-rust-early-stop-run1-kl0 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 "nmuendler/DeepSeek-R1-Distill-Qwen-7B-rust-early-stop-run1-kl0" \
    --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": "nmuendler/DeepSeek-R1-Distill-Qwen-7B-rust-early-stop-run1-kl0",
		"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 "nmuendler/DeepSeek-R1-Distill-Qwen-7B-rust-early-stop-run1-kl0" \
        --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": "nmuendler/DeepSeek-R1-Distill-Qwen-7B-rust-early-stop-run1-kl0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nmuendler/DeepSeek-R1-Distill-Qwen-7B-rust-early-stop-run1-kl0 with Docker Model Runner:
```
docker model run hf.co/nmuendler/DeepSeek-R1-Distill-Qwen-7B-rust-early-stop-run1-kl0
```

DeepSeek-R1-Distill-Qwen-7B-rust-early-stop-run1-kl0 / training_runtime.json

nmuendler

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	{
	"event": "training_done",
	"status": "success",
	"phase": "sft",
	"base_model": "/iopsstor/scratch/cscs/nmuendler/hf-cache/hub/models--deepseek-ai--DeepSeek-R1-Distill-Qwen-7B/snapshots/916b56a44061fd5cd7d6a8fb632557ed4f724f60",
	"train_file": "datasets/training_set_filtered_code_max.jsonl",
	"output_dir": "/capstor/scratch/cscs/nmuendler/reasoning_training2/outputs/reasoning_abort_sft/r1_qwen/rust_kl0_eb16_e5_lr2e-04/run1/lr2e-04/adapter",
	"task": "rust",
	"learning_rate": 0.0002,
	"epochs": 5,
	"batch_size": 2,
	"gradient_accumulation_steps": 8,
	"effective_batch_size": 16,
	"max_length": 3000,
	"target_mode": "standard",
	"rust_prompt_variant": "current",
	"full_finetune": false,
	"kl_coefficient": 0.0,
	"kl_temperature": 1.0,
	"kl_mask_mode": "full",
	"last_checkpoint": null,
	"function_started_at": "2026-09-14T20:28:44.298139+00:00",
	"training_started_at": "2026-09-14T20:29:00.860202+00:00",
	"ended_at": "2026-09-14T20:31:27.587191+00:00",
	"wall_clock_seconds": 146.72701206800411,
	"seconds": 146.72701206800411,
	"end_to_end_seconds": 163.2890692189976,
	"trainer_train_runtime": 145.073,
	"global_step": 4,
	"trainer_metrics": {
	"train_runtime": 145.073,
	"train_samples_per_second": 233.021,
	"train_steps_per_second": 14.579,
	"total_flos": 1176546930370560.0,
	"train_loss": 1.6097228229045868,
	"epoch": 0.009464655427388346
	}
	}