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

File size: 1,955 Bytes

---
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

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[More Information Needed]

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

[More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

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#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

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### Results

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#### Summary