Instructions to use AgentGYM-exp/syndra-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AgentGYM-exp/syndra-medium with Transformers:

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

pipe = pipeline("text-generation", model="AgentGYM-exp/syndra-medium")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("AgentGYM-exp/syndra-medium", dtype="auto")

PEFT
How to use AgentGYM-exp/syndra-medium with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AgentGYM-exp/syndra-medium with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/AgentGYM-exp/syndra-medium

SGLang

How to use AgentGYM-exp/syndra-medium 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 "AgentGYM-exp/syndra-medium" \
    --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": "AgentGYM-exp/syndra-medium",
		"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 "AgentGYM-exp/syndra-medium" \
        --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": "AgentGYM-exp/syndra-medium",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AgentGYM-exp/syndra-medium with Docker Model Runner:
```
docker model run hf.co/AgentGYM-exp/syndra-medium
```

victormaricato commited on Oct 29, 2025

Commit

bddc2f1

verified ·

1 Parent(s): 9f995ff

Upload folder using huggingface_hub

Browse files

Files changed (3) hide show

adapter_config.json +3 -3
adapter_model.safetensors +1 -1
training_args.bin +1 -1

adapter_config.json CHANGED Viewed

@@ -24,12 +24,12 @@
   "revision": null,
   "target_modules": [
     "down_proj",
     "q_proj",
     "o_proj",
     "v_proj",
-    "up_proj",
-    "k_proj",
-    "gate_proj"
   ],
   "task_type": "CAUSAL_LM",
   "use_dora": false,

   "revision": null,
   "target_modules": [
     "down_proj",
+    "gate_proj",
     "q_proj",
+    "up_proj",
     "o_proj",
     "v_proj",
+    "k_proj"
   ],
   "task_type": "CAUSAL_LM",
   "use_dora": false,

adapter_model.safetensors CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c984d19875fe5256064d537d7f9c8581d729c7ced559a1228fff3cdd1f8f78c1
 size 1685278680

 version https://git-lfs.github.com/spec/v1
+oid sha256:b27a8e4a7c6b8af46ee49da767121b333bda905e8ed1645b95b19d3a82f1597c
 size 1685278680

training_args.bin CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:06fe4bf80cd6b446261cf14168da6465a094c9857e5ba033d2049563577c4411
 size 5688

 version https://git-lfs.github.com/spec/v1
+oid sha256:06fe886746af826c7dc494d8e63e869b5ac36c64c5dac8a52b3eb70e04010190
 size 5688