Instructions to use psikosen/canopy-258m-r3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use psikosen/canopy-258m-r3 with Transformers:

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

pipe = pipeline("text-generation", model="psikosen/canopy-258m-r3", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("psikosen/canopy-258m-r3", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use psikosen/canopy-258m-r3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "psikosen/canopy-258m-r3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "psikosen/canopy-258m-r3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/psikosen/canopy-258m-r3

SGLang

How to use psikosen/canopy-258m-r3 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 "psikosen/canopy-258m-r3" \
    --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": "psikosen/canopy-258m-r3",
		"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 "psikosen/canopy-258m-r3" \
        --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": "psikosen/canopy-258m-r3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use psikosen/canopy-258m-r3 with Docker Model Runner:
```
docker model run hf.co/psikosen/canopy-258m-r3
```

canopy-258m-r3

File size: 637 Bytes

ed79a7f

{
  "production_promoted": false,
  "release_baseline_unchanged": true,
  "model_training": false,
  "result": "Application-specific state contract improves verification; retain as opt-in experimental adapter pending real-site integration.",
  "summary": {
    "total": 72,
    "baseline_correct": 47,
    "candidate_correct": 72,
    "baseline_false_success": 18,
    "candidate_false_success": 0,
    "baseline_verified_completions": 1,
    "candidate_verified_completions": 8,
    "weights_unchanged": true
  },
  "scope": "72 result-verification decisions: 8 verified completions and 64 refusals; not 72 completed browsing goals."
}