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 22

Commit

496fc07

verified ·

1 Parent(s): 4d657a7

update endpoint helper files

Browse files

Files changed (1) hide show

generate_dataset.py +31 -7

generate_dataset.py CHANGED Viewed

@@ -22,13 +22,37 @@ TEMPLATES = [
         "output": "if x == 10: print(x)",
         "explanation": "Corrected assignment to comparison operator.",
     },
-    {
-        "instruction": "Explain code",
-        "input": "for i in range(3): print(i)",
-        "output": "Prints numbers from 0 to 2.",
-        "explanation": "Loop iterates from 0 to 2 and prints values.",
-    },
-]
 def format_training_text(template):

         "output": "if x == 10: print(x)",
         "explanation": "Corrected assignment to comparison operator.",
     },
+    {
+        "instruction": "Explain code",
+        "input": "for i in range(3): print(i)",
+        "output": "Prints numbers from 0 to 2.",
+        "explanation": "Loop iterates from 0 to 2 and prints values.",
+    },
+    {
+        "instruction": "Write a Python function",
+        "input": "Create a function to multiply two numbers",
+        "output": "def multiply(a, b):\n    return a * b",
+        "explanation": "Defined a multiply function that returns the product of two inputs.",
+    },
+    {
+        "instruction": "Write a Python function",
+        "input": "Create a function to add two numbers",
+        "output": "def add(a, b):\n    return a + b",
+        "explanation": "Defined an add function that returns the sum of two inputs.",
+    },
+    {
+        "instruction": "Write a Python function",
+        "input": "Create a function to subtract two numbers",
+        "output": "def subtract(a, b):\n    return a - b",
+        "explanation": "Defined a subtract function that returns the difference between two inputs.",
+    },
+    {
+        "instruction": "Write a Python function",
+        "input": "Create a function to divide two numbers",
+        "output": "def divide(a, b):\n    return a / b",
+        "explanation": "Defined a divide function that returns the quotient of two inputs.",
+    },
+]
 def format_training_text(template):