Instructions to use tomasmcm/sky-t1-coder-32b-flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tomasmcm/sky-t1-coder-32b-flash with Transformers:

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

pipe = pipeline("text-generation", model="tomasmcm/sky-t1-coder-32b-flash")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tomasmcm/sky-t1-coder-32b-flash")
model = AutoModelForCausalLM.from_pretrained("tomasmcm/sky-t1-coder-32b-flash", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tomasmcm/sky-t1-coder-32b-flash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tomasmcm/sky-t1-coder-32b-flash"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tomasmcm/sky-t1-coder-32b-flash",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tomasmcm/sky-t1-coder-32b-flash

SGLang

How to use tomasmcm/sky-t1-coder-32b-flash 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 "tomasmcm/sky-t1-coder-32b-flash" \
    --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": "tomasmcm/sky-t1-coder-32b-flash",
		"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 "tomasmcm/sky-t1-coder-32b-flash" \
        --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": "tomasmcm/sky-t1-coder-32b-flash",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tomasmcm/sky-t1-coder-32b-flash with Docker Model Runner:
```
docker model run hf.co/tomasmcm/sky-t1-coder-32b-flash
```

tomasmcm commited on Mar 15, 2025

Commit

5460d16

verified ·

1 Parent(s): d336471

Update README.md

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README.md CHANGED Viewed

@@ -18,6 +18,18 @@ I wanted to see if it would be possible to improve on [FuseO1-DeepSeekR1-Qwen2.5
 GGUF files available at [mradermacher/sky-t1-coder-32b-flash-GGUF](https://huggingface.co/mradermacher/sky-t1-coder-32b-flash-GGUF) (thank you!)
 ## Merge Details
 ### Merge Method

 GGUF files available at [mradermacher/sky-t1-coder-32b-flash-GGUF](https://huggingface.co/mradermacher/sky-t1-coder-32b-flash-GGUF) (thank you!)
+## LLM Leaderboard (15 Mar 2025)
+| Rank | Model | Average | IFEval | BBH | MATH | GPQA | MUSR | MMLU-PRO |
+|------|--------|---------|---------|-----|------|------|------|-----------|
+| 22 | Qwen/Qwen2.5-32B-Instruct | **46.60%** | **83.46%** | **56.49%** | **62.54%** | 11.74% | 13.50% | 51.85% |
+| 39 | tomasmcm/sky-t1-coder-32b-flash | 44.87% | 77.80% | 55.47% | 54.23% | **15.77%** | 12.81% | **53.13%** |
+| 147 | huihuiai/QwQ-32B-Coder-Fusion-9010 | 41.58% | 57.78% | 53.02% | 53.17% | 14.88% | **19.52%** | 51.11% |
+| 276 | Qwen/Qwen2.5-Coder-32B-Instruct | 39.89% | 72.65% | 52.27% | 49.55% | 13.20% | 13.72% | 37.92% |
+| 717 | Qwen/QwQ-32B-Preview | 34.12% | 40.35% | 53.39% | 44.94% | 4.25% | 9.81% | 51.98% |
+| 2100 | deepseek-ai/DeepSeek-R1-Distill-Qwen-32B | 22.96% | 41.86% | 17.15% | 17.07% | 4.59% | 16.14% | 40.96% |
+| 3574 | Qwen/QwQ-32B | 12.21% | 39.77% | 2.87% | 16.09% | 1.34% | 11.05% | 2.18% |
 ## Merge Details
 ### Merge Method