Instructions to use autoprogrammer/qwen3_8b-codev_r1_sft_python_passed_ckpt_2000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use autoprogrammer/qwen3_8b-codev_r1_sft_python_passed_ckpt_2000 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-8B")
model = PeftModel.from_pretrained(base_model, "autoprogrammer/qwen3_8b-codev_r1_sft_python_passed_ckpt_2000")

Transformers

How to use autoprogrammer/qwen3_8b-codev_r1_sft_python_passed_ckpt_2000 with Transformers:

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

pipe = pipeline("text-generation", model="autoprogrammer/qwen3_8b-codev_r1_sft_python_passed_ckpt_2000")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("autoprogrammer/qwen3_8b-codev_r1_sft_python_passed_ckpt_2000", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use autoprogrammer/qwen3_8b-codev_r1_sft_python_passed_ckpt_2000 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "autoprogrammer/qwen3_8b-codev_r1_sft_python_passed_ckpt_2000"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "autoprogrammer/qwen3_8b-codev_r1_sft_python_passed_ckpt_2000",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/autoprogrammer/qwen3_8b-codev_r1_sft_python_passed_ckpt_2000

SGLang

How to use autoprogrammer/qwen3_8b-codev_r1_sft_python_passed_ckpt_2000 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 "autoprogrammer/qwen3_8b-codev_r1_sft_python_passed_ckpt_2000" \
    --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": "autoprogrammer/qwen3_8b-codev_r1_sft_python_passed_ckpt_2000",
		"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 "autoprogrammer/qwen3_8b-codev_r1_sft_python_passed_ckpt_2000" \
        --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": "autoprogrammer/qwen3_8b-codev_r1_sft_python_passed_ckpt_2000",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use autoprogrammer/qwen3_8b-codev_r1_sft_python_passed_ckpt_2000 with Docker Model Runner:
```
docker model run hf.co/autoprogrammer/qwen3_8b-codev_r1_sft_python_passed_ckpt_2000
```

autoprogrammer commited on Mar 2

Commit

9e95338

verified ·

1 Parent(s): 91ee23c

Upload special_tokens_map.json with huggingface_hub

Browse files

Files changed (1) hide show

special_tokens_map.json +31 -0

special_tokens_map.json ADDED Viewed

	@@ -0,0 +1,31 @@

+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}