Instructions to use PARTAGES-dev/Qwen3-8B-PDAPT-SLERP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PARTAGES-dev/Qwen3-8B-PDAPT-SLERP with Transformers:

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

pipe = pipeline("text-generation", model="PARTAGES-dev/Qwen3-8B-PDAPT-SLERP")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PARTAGES-dev/Qwen3-8B-PDAPT-SLERP")
model = AutoModelForCausalLM.from_pretrained("PARTAGES-dev/Qwen3-8B-PDAPT-SLERP")
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

vLLM

How to use PARTAGES-dev/Qwen3-8B-PDAPT-SLERP with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PARTAGES-dev/Qwen3-8B-PDAPT-SLERP"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PARTAGES-dev/Qwen3-8B-PDAPT-SLERP",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PARTAGES-dev/Qwen3-8B-PDAPT-SLERP

SGLang

How to use PARTAGES-dev/Qwen3-8B-PDAPT-SLERP 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 "PARTAGES-dev/Qwen3-8B-PDAPT-SLERP" \
    --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": "PARTAGES-dev/Qwen3-8B-PDAPT-SLERP",
		"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 "PARTAGES-dev/Qwen3-8B-PDAPT-SLERP" \
        --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": "PARTAGES-dev/Qwen3-8B-PDAPT-SLERP",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use PARTAGES-dev/Qwen3-8B-PDAPT-SLERP with Docker Model Runner:
```
docker model run hf.co/PARTAGES-dev/Qwen3-8B-PDAPT-SLERP
```

a-mannion commited on Apr 8

Commit

85ea976

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1 Parent(s): 4ec20ff

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@@ -8,9 +8,18 @@ tags:
 ---
 # Qwen3-8B-PDAPT-SLERP
-This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
 ## Merge Details
 ### Merge Method
 This model was merged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method.

 ---
 # Qwen3-8B-PDAPT-SLERP
+## PARTAGES
+The PARTAGES project is a French national initiative aiming to develop specialized language models for use in the automation of document-processing tasks in the French healthcare system.
+All foundational PARTAGES data and models will be released as open-source tools.
+**Note:** While these models have been developed in the context of a healthcare-focused project, they are intended for research use only and should not under any circumstances be used as-is in production settings or as a replacement for medical advice of any kind.
 ## Merge Details
+This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
 ### Merge Method
 This model was merged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method.