Instructions to use jdopensource/JoyAI-LLM-Flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jdopensource/JoyAI-LLM-Flash with Transformers:

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

pipe = pipeline("text-generation", model="jdopensource/JoyAI-LLM-Flash", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("jdopensource/JoyAI-LLM-Flash", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use jdopensource/JoyAI-LLM-Flash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jdopensource/JoyAI-LLM-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": "jdopensource/JoyAI-LLM-Flash",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jdopensource/JoyAI-LLM-Flash

SGLang

How to use jdopensource/JoyAI-LLM-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 "jdopensource/JoyAI-LLM-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": "jdopensource/JoyAI-LLM-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 "jdopensource/JoyAI-LLM-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": "jdopensource/JoyAI-LLM-Flash",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jdopensource/JoyAI-LLM-Flash with Docker Model Runner:
```
docker model run hf.co/jdopensource/JoyAI-LLM-Flash
```

Update docs/deploy_guidance.md

by Mingke977 - opened Feb 15

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

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docs/deploy_guidance.md +15 -3

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@@ -8,17 +8,29 @@
 ## vLLM Deployment
 Here is the example to serve this model on a H200 single node with TP8 via vLLM:
 ```bash
-vllm serve ${MODEL_PATH} --tp 8 --trust-remote-code \
-    --tool-call-parser qwen3_coder --enable-auto-tool-choice \
-    --speculative-config $'{"method": "mtp", "num_speculative_tokens": 3}'
 ```
 **Key notes**
 - `--tool-call-parser qwen3_coder`: Required for enabling tool calling
 ## SGLang Deployment
 Similarly, here is the example to run with TP8 on H200 in a single node via SGLang:
 ```bash
 python3 -m sglang.launch_server --model-path ${MODEL_PATH} --tp-size 8 --trust-remote-code \
   --tool-call-parser qwen3_coder \

 ## vLLM Deployment
 Here is the example to serve this model on a H200 single node with TP8 via vLLM:
+1. pull the Docker image.
 ```bash
+docker pull jdopensource/joyai-llm-vllm:v0.13.0-joyai_llm_flash
 ```
+2. launch JoyAI-LLM Flash model with dense MTP.
+```bash
+vllm serve ${MODEL_PATH} --tp 8 --trust-remote-code \
+  --tool-call-parser qwen3_coder --enable-auto-tool-choice \
+  --speculative-config $'{"method": "mtp", "num_speculative_tokens": 3}'
 **Key notes**
 - `--tool-call-parser qwen3_coder`: Required for enabling tool calling
 ## SGLang Deployment
 Similarly, here is the example to run with TP8 on H200 in a single node via SGLang:
+1. pull the Docker image.
+```bash
+docker pull jdopensource/joyai-llm-sglang:v0.5.8-joyai_llm_flash
+```
+2. launch JoyAI-LLM Flash model with dense MTP.
 ```bash
 python3 -m sglang.launch_server --model-path ${MODEL_PATH} --tp-size 8 --trust-remote-code \
   --tool-call-parser qwen3_coder \