Instructions to use girish00/ConicAI_LLM_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use girish00/ConicAI_LLM_model with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-0.5B-Instruct")
model = PeftModel.from_pretrained(base_model, "girish00/ConicAI_LLM_model")

Transformers

How to use girish00/ConicAI_LLM_model with Transformers:

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

pipe = pipeline("text-generation", model="girish00/ConicAI_LLM_model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("girish00/ConicAI_LLM_model")
model = AutoModelForCausalLM.from_pretrained("girish00/ConicAI_LLM_model")
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 girish00/ConicAI_LLM_model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "girish00/ConicAI_LLM_model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "girish00/ConicAI_LLM_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/girish00/ConicAI_LLM_model

SGLang

How to use girish00/ConicAI_LLM_model 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 "girish00/ConicAI_LLM_model" \
    --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": "girish00/ConicAI_LLM_model",
		"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 "girish00/ConicAI_LLM_model" \
        --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": "girish00/ConicAI_LLM_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use girish00/ConicAI_LLM_model with Docker Model Runner:
```
docker model run hf.co/girish00/ConicAI_LLM_model
```

girish00 commited on Apr 19

Commit

89a3adc

verified ·

1 Parent(s): 48ea9da

make project runnable and endpoint-ready

Browse files

Files changed (1) hide show

upload_to_hf.py +34 -9

upload_to_hf.py CHANGED Viewed

@@ -1,14 +1,29 @@
-import argparse
-import os
-from huggingface_hub import upload_folder
 def main():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--force", action="store_true")
-    parser.add_argument("--model-dir", type=str, default="./model")
-    parser.add_argument("--repo-id", type=str, required=True)
-    args = parser.parse_args()
     if not os.path.exists(args.model_dir):
         raise FileNotFoundError(
@@ -20,9 +35,19 @@ def main():
         repo_id=args.repo_id,
         repo_type="model",
         commit_message="update model",
-        ignore_patterns=[]  # ensures upload always checks properly
     )
-    print(f"Uploaded model to: {args.repo_id}")
 if __name__ == "__main__":

+import argparse
+import os
+from huggingface_hub import upload_file, upload_folder
+PROJECT_FILES = [
+    "handler.py",
+    "infer_local.py",
+    "infer_cloud.py",
+    "requirements.txt",
+    "README.md",
+    "IMPLEMENTATION.md",
+]
 def main():
     parser = argparse.ArgumentParser()
+    parser.add_argument("--force", action="store_true")
+    parser.add_argument("--model-dir", type=str, default="./model")
+    parser.add_argument("--repo-id", type=str, required=True)
+    parser.add_argument(
+        "--model-only",
+        action="store_true",
+        help="Upload only model artifacts and skip endpoint/helper project files.",
+    )
+    args = parser.parse_args()
     if not os.path.exists(args.model_dir):
         raise FileNotFoundError(
         repo_id=args.repo_id,
         repo_type="model",
         commit_message="update model",
+        ignore_patterns=["checkpoint-*"] if not args.force else [],
     )
+    if not args.model_only:
+        for path in PROJECT_FILES:
+            if os.path.exists(path):
+                upload_file(
+                    path_or_fileobj=path,
+                    path_in_repo=path,
+                    repo_id=args.repo_id,
+                    repo_type="model",
+                    commit_message="update endpoint helper files",
+                )
+    print(f"Uploaded model to: {args.repo_id}")
 if __name__ == "__main__":