Instructions to use jnaglick/Qwen2.5-Coder-7B-Instruct-Roku with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jnaglick/Qwen2.5-Coder-7B-Instruct-Roku with Transformers:

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

pipe = pipeline("text-generation", model="jnaglick/Qwen2.5-Coder-7B-Instruct-Roku")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("jnaglick/Qwen2.5-Coder-7B-Instruct-Roku", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jnaglick/Qwen2.5-Coder-7B-Instruct-Roku with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jnaglick/Qwen2.5-Coder-7B-Instruct-Roku"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jnaglick/Qwen2.5-Coder-7B-Instruct-Roku",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jnaglick/Qwen2.5-Coder-7B-Instruct-Roku

SGLang

How to use jnaglick/Qwen2.5-Coder-7B-Instruct-Roku 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 "jnaglick/Qwen2.5-Coder-7B-Instruct-Roku" \
    --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": "jnaglick/Qwen2.5-Coder-7B-Instruct-Roku",
		"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 "jnaglick/Qwen2.5-Coder-7B-Instruct-Roku" \
        --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": "jnaglick/Qwen2.5-Coder-7B-Instruct-Roku",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jnaglick/Qwen2.5-Coder-7B-Instruct-Roku with Docker Model Runner:
```
docker model run hf.co/jnaglick/Qwen2.5-Coder-7B-Instruct-Roku
```

Qwen2.5-Coder-7B-Instruct-Roku / README.md

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metadata

library_name: transformers
pipeline_tag: text-generation
tags: []

Model Card for Model ID

Qwen2.5-Coder-7B-Instruct fine-tuned on code and documentation from the Roku ecosystem. This includes Bright[er]Script code, Scenegraph XML templates, and documentation describing the standard API(s).

Model Details

Model Description

Developed by: John M Naglick jmn@econify.com
Model type: text-generation (code, instruct)
Finetuned from model [optional]: Qwen2.5-Coder-7B-Instruct

Training Details

Training Data

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

[More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

jnaglick
/

Qwen2.5-Coder-7B-Instruct-Roku

Model Card for Model ID

Model Details

Model Description

Training Details

Training Data

Training Procedure

Preprocessing [optional]

Training Hyperparameters

Speeds, Sizes, Times [optional]

Evaluation

Testing Data, Factors & Metrics

Testing Data

Factors

Metrics

Results

Summary