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 Settings

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

ec50606

verified ·

1 Parent(s): 36ff300

add structured endpoint handler

Browse files

Files changed (1) hide show

IMPLEMENTATION.md +17 -1

IMPLEMENTATION.md CHANGED Viewed

@@ -27,6 +27,9 @@ Build and run a local fine-tuning pipeline for a coding assistant model with:
   - Runs inference through the Hugging Face API using an HF token.
   - Reuses the local structured-output parser and repair checks so API output matches the local JSON contract.
   - Falls back to the local `model/` folder when Hugging Face does not serve the custom repo through an inference provider.
 - `evaluate_model.py`
   - Runs a multi-prompt evaluation and reports pass rate (accuracy) for schema + quality checks.
 - `upload_to_hf.py`
@@ -105,7 +108,7 @@ To update an already published Hugging Face model with current project behavior:
 Optional safer rollout:
 - Upload to a revision branch first and test before merging to main.
-## Current Output Contract
 `infer_local.py` returns JSON with:
 - `code`
@@ -118,3 +121,16 @@ Optional safer rollout:
 - `latency_ms`
 `infer_cloud.py` returns the same JSON keys through the Hugging Face API, or through local fallback if HF cannot serve the custom repo. Cloud responses may not include token-level probabilities, so `important_tokens` can be empty and `confidence` can be `0.0` unless the serving endpoint exposes token details.

   - Runs inference through the Hugging Face API using an HF token.
   - Reuses the local structured-output parser and repair checks so API output matches the local JSON contract.
   - Falls back to the local `model/` folder when Hugging Face does not serve the custom repo through an inference provider.
+- `handler.py`
+  - Custom Hugging Face Dedicated Inference Endpoint handler.
+  - Loads the LoRA adapter/full model and returns the same structured JSON contract directly from the hosted endpoint.
 - `evaluate_model.py`
   - Runs a multi-prompt evaluation and reports pass rate (accuracy) for schema + quality checks.
 - `upload_to_hf.py`
 Optional safer rollout:
 - Upload to a revision branch first and test before merging to main.
+## Current Output Contract
 `infer_local.py` returns JSON with:
 - `code`
 - `latency_ms`
 `infer_cloud.py` returns the same JSON keys through the Hugging Face API, or through local fallback if HF cannot serve the custom repo. Cloud responses may not include token-level probabilities, so `important_tokens` can be empty and `confidence` can be `0.0` unless the serving endpoint exposes token details.
+For users calling the hosted model with their own token/API key, deploy the repository as a Hugging Face Dedicated Inference Endpoint. The included `handler.py` makes endpoint responses use the same JSON pattern:
+- `code`
+- `explanation`
+- `confidence`
+- `important_tokens`
+- `relevancy_score`
+- `hallucination`
+- `hallucination_check_reason`
+- `latency_ms`
+Direct Hugging Face serverless calls to the model repo are not guaranteed to run custom LoRA repos. Dedicated endpoints or a cloud VM are required for true cloud execution.