Instructions to use sinhapiyush86/convAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sinhapiyush86/convAI with Transformers:

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

pipe = pipeline("text-generation", model="sinhapiyush86/convAI")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sinhapiyush86/convAI")
model = AutoModelForCausalLM.from_pretrained("sinhapiyush86/convAI")
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 sinhapiyush86/convAI with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sinhapiyush86/convAI"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sinhapiyush86/convAI",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sinhapiyush86/convAI

SGLang

How to use sinhapiyush86/convAI 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 "sinhapiyush86/convAI" \
    --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": "sinhapiyush86/convAI",
		"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 "sinhapiyush86/convAI" \
        --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": "sinhapiyush86/convAI",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sinhapiyush86/convAI with Docker Model Runner:
```
docker model run hf.co/sinhapiyush86/convAI
```

sinhapiyush86 commited on Aug 24, 2025

Commit

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@@ -1,5 +1,31 @@
----
-license: apache-2.0
-tags:
-- llama-factory
----

+---
+language:
+- en
+license: apache-2.0
+tags:
+- chat
+- qwen
+- instruct
+- lora
+- fine-tuned
+- transformers
+pipeline_tag: text-generation
+library_name: transformers
+---
+# LoRA Fine-Tuned Qwen2.5-1.5B-Instruct
+This model is a **LoRA fine-tuned version** of [Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct), optimized for instruction-following tasks.
+- **Base model:** `Qwen/Qwen2.5-1.5B-Instruct`
+- **Method:** Parameter-efficient fine-tuning with [PEFT (LoRA)](https://huggingface.co/docs/peft)
+- **Framework:** 🤗 Transformers + PEFT
+- **Use case:** Conversational AI, instruction following, Q&A
+---
+## 🚀 Usage
+### Install dependencies
+```bash
+pip install transformers accelerate peft