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

00af942

verified ·

1 Parent(s): 0b85305

Update README.md

Browse files

Files changed (1) hide show

README.md +38 -38

README.md CHANGED Viewed

@@ -79,44 +79,6 @@ The model focuses on three key aspects:
 ---
-## Training Details
-### Dataset
-* Size: ~5K–10K samples
-* Instruction-based coding dataset
-### Training Procedure
-* Method: LoRA fine-tuning
-* Framework: Transformers + PEFT
-* Precision: FP16 / Mixed
-### Training Hyperparameters
-| Parameter           | Value |
-| ------------------- | ----- |
-| Epochs              | 1–3   |
-| Batch Size          | 2     |
-| Learning Rate       | 2e-4  |
-| Max Sequence Length | 512   |
-| LoRA Rank (r)       | 8     |
-| LoRA Alpha          | 16    |
-| LoRA Dropout        | 0.05  |
----
-## Inference Configuration
-```text
-max_new_tokens = 200
-temperature = 0.2
-top_p = 0.9
-do_sample = True
-```
----
 ## 🧪 How to Use This Model (Colab / Local)
 ```python
@@ -189,6 +151,44 @@ print(json.dumps(
 ---
 ## Evaluation
 * Syntax validation

 ---
 ## 🧪 How to Use This Model (Colab / Local)
 ```python
 ---
+## Training Details
+### Dataset
+* Size: ~5K–10K samples
+* Instruction-based coding dataset
+### Training Procedure
+* Method: LoRA fine-tuning
+* Framework: Transformers + PEFT
+* Precision: FP16 / Mixed
+### Training Hyperparameters
+| Parameter           | Value |
+| ------------------- | ----- |
+| Epochs              | 1–3   |
+| Batch Size          | 2     |
+| Learning Rate       | 2e-4  |
+| Max Sequence Length | 512   |
+| LoRA Rank (r)       | 8     |
+| LoRA Alpha          | 16    |
+| LoRA Dropout        | 0.05  |
+---
+## Inference Configuration
+```text
+max_new_tokens = 200
+temperature = 0.2
+top_p = 0.9
+do_sample = True
+```
+---
 ## Evaluation
 * Syntax validation