Instructions to use Sandroeth/cali-0.1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sandroeth/cali-0.1B with Transformers:

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

pipe = pipeline("text-generation", model="Sandroeth/cali-0.1B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Sandroeth/cali-0.1B", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Sandroeth/cali-0.1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sandroeth/cali-0.1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sandroeth/cali-0.1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Sandroeth/cali-0.1B

SGLang

How to use Sandroeth/cali-0.1B 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 "Sandroeth/cali-0.1B" \
    --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": "Sandroeth/cali-0.1B",
		"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 "Sandroeth/cali-0.1B" \
        --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": "Sandroeth/cali-0.1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Sandroeth/cali-0.1B with Docker Model Runner:
```
docker model run hf.co/Sandroeth/cali-0.1B
```

Sandroeth commited on 4 days ago

Commit

75b4fe7

verified ·

1 Parent(s): 69654ae

Update modeling_cali.py

Browse files

Files changed (1) hide show

modeling_cali.py +4 -1

modeling_cali.py CHANGED Viewed

@@ -234,7 +234,7 @@ class CALIModel(CALIPreTrainedModel):
 class CALIForCausalLM(CALIPreTrainedModel, GenerationMixin):
-    _tied_weights_keys = ["lm_head.weight"]
     def __init__(self, config: CALIConfig):
         super().__init__(config)
@@ -250,6 +250,9 @@ class CALIForCausalLM(CALIPreTrainedModel, GenerationMixin):
     def set_input_embeddings(self, value):
         self.model.embed = value
     def get_output_embeddings(self):
         return self.lm_head

 class CALIForCausalLM(CALIPreTrainedModel, GenerationMixin):
     def __init__(self, config: CALIConfig):
         super().__init__(config)
     def set_input_embeddings(self, value):
         self.model.embed = value
+    def get_tied_weights(self):
+        return {"lm_head.weight": "model.embed.weight"} if self.config.tie_embeddings else {}
     def get_output_embeddings(self):
         return self.lm_head