Instructions to use Rumiii/Qwen2.5-AlphaCoder-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Rumiii/Qwen2.5-AlphaCoder-14B with Transformers:

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

pipe = pipeline("text-generation", model="Rumiii/Qwen2.5-AlphaCoder-14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Rumiii/Qwen2.5-AlphaCoder-14B")
model = AutoModelForCausalLM.from_pretrained("Rumiii/Qwen2.5-AlphaCoder-14B", 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 Rumiii/Qwen2.5-AlphaCoder-14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Rumiii/Qwen2.5-AlphaCoder-14B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Rumiii/Qwen2.5-AlphaCoder-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Rumiii/Qwen2.5-AlphaCoder-14B

SGLang

How to use Rumiii/Qwen2.5-AlphaCoder-14B 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 "Rumiii/Qwen2.5-AlphaCoder-14B" \
    --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": "Rumiii/Qwen2.5-AlphaCoder-14B",
		"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 "Rumiii/Qwen2.5-AlphaCoder-14B" \
        --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": "Rumiii/Qwen2.5-AlphaCoder-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Rumiii/Qwen2.5-AlphaCoder-14B with Docker Model Runner:
```
docker model run hf.co/Rumiii/Qwen2.5-AlphaCoder-14B
```

Qwen2.5-AlphaCoder-14B

An experimental fine-tune of Qwen2.5-Coder-14B-Instruct that explores lightweight distillation: transferring the agentic, tool-calling behavior of Qwen3-32B into a smaller Qwen2.5 model.

Method

Teacher data: 500 Qwen3-32B tool-use trajectories from the Qwen3 subset of Toucan-1.5M (multi-turn, real MCP tool calls)
Training: QLoRA (r=16, alpha=32), 2 epochs, loss on assistant tokens only
Context: whole trajectories up to 2,048 tokens, no truncation
Format: Qwen2.5-native <tool_call> / <tool_response> tags

Files

Root: 4-bit (bitsandbytes) model weights
adapter/: the QLoRA adapter on its own

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "Rumiii/Qwen2.5-AlphaCoder-14B"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, device_map="auto")  # needs bitsandbytes + GPU

# pass `tools=[...]` to tok.apply_chat_template for function calling

Limitations

This is a small proof-of-concept run (500 samples). It has not been benchmarked, and it should not be expected to match the capability of the 32B teacher.

License

Apache 2.0, following the base model and dataset.

Downloads last month: 184

Safetensors

Model size

15B params

Tensor type

F16

Model tree for Rumiii/Qwen2.5-AlphaCoder-14B

Base model

Qwen/Qwen2.5-14B

Finetuned

Qwen/Qwen2.5-Coder-14B

Finetuned

Qwen/Qwen2.5-Coder-14B-Instruct

Quantized

(132)

this model

Rumiii
/

Qwen2.5-AlphaCoder-14B