Instructions to use autoprogrammer/qwen3_8b-codev_r1_sft_python_passed_ckpt_1500 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_1500 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_1500")

Transformers

How to use autoprogrammer/qwen3_8b-codev_r1_sft_python_passed_ckpt_1500 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_1500")
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_1500", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use autoprogrammer/qwen3_8b-codev_r1_sft_python_passed_ckpt_1500 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_1500"
# 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_1500",
		"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_1500

SGLang

How to use autoprogrammer/qwen3_8b-codev_r1_sft_python_passed_ckpt_1500 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_1500" \
    --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_1500",
		"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_1500" \
        --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_1500",
		"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_1500 with Docker Model Runner:
```
docker model run hf.co/autoprogrammer/qwen3_8b-codev_r1_sft_python_passed_ckpt_1500
```

autoprogrammer commited on Mar 2

Commit

caa64ad

verified ·

1 Parent(s): 95c16f0

Upload adapter_config.json with huggingface_hub

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Files changed (1) hide show

adapter_config.json +42 -0

adapter_config.json ADDED Viewed

	@@ -0,0 +1,42 @@

+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "Qwen/Qwen3-8B",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "qalora_group_size": 16,
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "v_proj",
+    "up_proj",
+    "down_proj",
+    "k_proj",
+    "o_proj",
+    "gate_proj",
+    "q_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}