Instructions to use prithivMLmods/Llama-Chat-Summary-3.2-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Llama-Chat-Summary-3.2-3B with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/Llama-Chat-Summary-3.2-3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Llama-Chat-Summary-3.2-3B")
model = AutoModelForMultimodalLM.from_pretrained("prithivMLmods/Llama-Chat-Summary-3.2-3B")
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 prithivMLmods/Llama-Chat-Summary-3.2-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Llama-Chat-Summary-3.2-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Llama-Chat-Summary-3.2-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Llama-Chat-Summary-3.2-3B

SGLang

How to use prithivMLmods/Llama-Chat-Summary-3.2-3B 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 "prithivMLmods/Llama-Chat-Summary-3.2-3B" \
    --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": "prithivMLmods/Llama-Chat-Summary-3.2-3B",
		"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 "prithivMLmods/Llama-Chat-Summary-3.2-3B" \
        --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": "prithivMLmods/Llama-Chat-Summary-3.2-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/Llama-Chat-Summary-3.2-3B with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Llama-Chat-Summary-3.2-3B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Llama-Chat-Summary-3.2-3B: Context-Aware Summarization Model

Llama-Chat-Summary-3.2-3B is a fine-tuned model designed for generating context-aware summaries of long conversational or text-based inputs. Built on the meta-llama/Llama-3.2-3B-Instruct foundation, this model is optimized to process structured and unstructured conversational data for summarization tasks.

File Name	Size	Description	Upload Status
`.gitattributes`	1.57 kB	Git LFS tracking configuration.	Uploaded
`README.md`	42 Bytes	Initial commit with minimal documentation.	Uploaded
`config.json`	1.03 kB	Model configuration settings.	Uploaded
`generation_config.json`	248 Bytes	Generation-specific configurations.	Uploaded
`pytorch_model-00001-of-00002.bin`	4.97 GB	Part 1 of the PyTorch model weights.	Uploaded (LFS)
`pytorch_model-00002-of-00002.bin`	1.46 GB	Part 2 of the PyTorch model weights.	Uploaded (LFS)
`pytorch_model.bin.index.json`	21.2 kB	Index file for the model weights.	Uploaded
`special_tokens_map.json`	477 Bytes	Mapping of special tokens for the tokenizer.	Uploaded
`tokenizer.json`	17.2 MB	Pre-trained tokenizer file.	Uploaded (LFS)
`tokenizer_config.json`	57.4 kB	Configuration file for the tokenizer.	Uploaded

Key Features

Conversation Summarization:
- Generates concise and meaningful summaries of long chats, discussions, or threads.
Context Preservation:
- Maintains critical points, ensuring important details aren't omitted.
Text Summarization:
- Works beyond chats; supports summarizing articles, documents, or reports.
Fine-Tuned Efficiency:
- Trained with Context-Based-Chat-Summary-Plus dataset for accurate summarization of chat and conversational data.

Training Details

Base Model: meta-llama/Llama-3.2-3B-Instruct
Fine-Tuning Dataset: prithivMLmods/Context-Based-Chat-Summary-Plus
- Contains 98.4k structured and unstructured conversations, summaries, and contextual inputs for robust training.

Applications

Customer Support Logs:
- Summarize chat logs or support tickets for insights and reporting.
Meeting Notes:
- Generate concise summaries of meeting transcripts.
Document Summarization:
- Create short summaries for lengthy reports or articles.
Content Generation Pipelines:
- Automate summarization for newsletters, blogs, or email digests.
Context Extraction for AI Systems:
- Preprocess chat or conversation logs for downstream AI applications.

Load the Model

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Llama-Chat-Summary-3.2-3B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

Generate a Summary

prompt = """
Summarize the following conversation:
User1: Hey, I need help with my order. It hasn't arrived yet.
User2: I'm sorry to hear that. Can you provide your order number?
User1: Sure, it's 12345.
User2: Let me check... It seems there was a delay. It should arrive tomorrow.
User1: Okay, thank you!
"""

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100, temperature=0.7)

summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
print("Summary:", summary)

Expected Output

"The user reported a delayed order (12345), and support confirmed it will arrive tomorrow."

Deployment Notes

Serverless API:
This model currently lacks sufficient usage for serverless endpoints. Use dedicated endpoints for deployment.
Performance Requirements:
- GPU with sufficient memory (recommended for large models).
- Optimization techniques like quantization can improve efficiency for inference.