Instructions to use FineEnvs/Qwen3.5-2B-opencode-standalone-RL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FineEnvs/Qwen3.5-2B-opencode-standalone-RL with Transformers:

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

pipe = pipeline("text-generation", model="FineEnvs/Qwen3.5-2B-opencode-standalone-RL")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("FineEnvs/Qwen3.5-2B-opencode-standalone-RL")
model = AutoModelForMultimodalLM.from_pretrained("FineEnvs/Qwen3.5-2B-opencode-standalone-RL", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FineEnvs/Qwen3.5-2B-opencode-standalone-RL with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FineEnvs/Qwen3.5-2B-opencode-standalone-RL"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FineEnvs/Qwen3.5-2B-opencode-standalone-RL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FineEnvs/Qwen3.5-2B-opencode-standalone-RL

SGLang

How to use FineEnvs/Qwen3.5-2B-opencode-standalone-RL 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 "FineEnvs/Qwen3.5-2B-opencode-standalone-RL" \
    --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": "FineEnvs/Qwen3.5-2B-opencode-standalone-RL",
		"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 "FineEnvs/Qwen3.5-2B-opencode-standalone-RL" \
        --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": "FineEnvs/Qwen3.5-2B-opencode-standalone-RL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FineEnvs/Qwen3.5-2B-opencode-standalone-RL with Docker Model Runner:
```
docker model run hf.co/FineEnvs/Qwen3.5-2B-opencode-standalone-RL
```

AdithyaSK HF Staff commited on 20 days ago

Commit

73031e6

verified ·

1 Parent(s): 03d1914

Fix disconnected evaluation lines and GIF previews

Browse files

Files changed (3) hide show

README.md +2 -2
assets/curves.gif +2 -2
assets/curves.png +2 -2

README.md CHANGED Viewed

@@ -97,9 +97,9 @@ A full fine-tune of [Qwen/Qwen3.5-2B](https://huggingface.co/Qwen/Qwen3.5-2B) fo
 ## Training and evaluation curves
-[![Animated training, pass@1 and tool-use curves through step 1,000](https://huggingface.co/FineEnvs/Qwen3.5-2B-opencode-standalone-RL/resolve/main/assets/curves.gif)](https://huggingface.co/spaces/AdithyaSK/multi-harness-rl)
-Training curves use a trailing 50-step mean (at least 10 observations). Evaluation markers show measured checkpoints; hollow markers indicate incomplete coverage. The tool-use panel shows absolute calls per graded evaluation rollout; this Qwen run used correctness-only rewards.  The animation stops at this revision's step 1,000.
 [Static chart](https://huggingface.co/FineEnvs/Qwen3.5-2B-opencode-standalone-RL/resolve/main/assets/curves.png) · [Plotted data](https://huggingface.co/FineEnvs/Qwen3.5-2B-opencode-standalone-RL/resolve/main/assets/curves.json) · [Interactive article](https://huggingface.co/spaces/AdithyaSK/multi-harness-rl)

 ## Training and evaluation curves
+[![Animated training, pass@1 and tool-use curves through step 1,000](https://huggingface.co/FineEnvs/Qwen3.5-2B-opencode-standalone-RL/resolve/main/assets/curves.gif?v=2)](https://huggingface.co/spaces/AdithyaSK/multi-harness-rl)
+Training curves use a trailing 50-step mean (at least 10 observations). Evaluation markers show measured checkpoints; hollow markers and dotted segments indicate incomplete coverage. Faint background lines show the full recorded trajectory; colored lines reveal the measured checkpoints. The first frame shows the completed chart before replaying, so previews also contain the full curves. The tool-use panel shows absolute calls per graded evaluation rollout; this Qwen run used correctness-only rewards.  The animation stops at this revision's step 1,000.
 [Static chart](https://huggingface.co/FineEnvs/Qwen3.5-2B-opencode-standalone-RL/resolve/main/assets/curves.png) · [Plotted data](https://huggingface.co/FineEnvs/Qwen3.5-2B-opencode-standalone-RL/resolve/main/assets/curves.json) · [Interactive article](https://huggingface.co/spaces/AdithyaSK/multi-harness-rl)

assets/curves.gif CHANGED Viewed

Git LFS Details

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Pointer size: 132 Bytes
Size of remote file: 3.29 MB

Git LFS Details

SHA256: e566302f0d3960bce10a090baf7592df3bf05876b359e95fc3128430d10cb387
Pointer size: 132 Bytes
Size of remote file: 4.06 MB

assets/curves.png CHANGED Viewed

Git LFS Details

SHA256: 200a4f43bfb19b50a54642f368f72aeff388f5cd3d252d21d6b96b31fc555bab
Pointer size: 131 Bytes
Size of remote file: 156 kB

Git LFS Details

SHA256: e849e08c578cd26793483f2a341cd6f824c5f348006599290d903936ac087e2c
Pointer size: 131 Bytes
Size of remote file: 161 kB