Instructions to use RinggAI/Transcript-Analytics-SLM0.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RinggAI/Transcript-Analytics-SLM0.5b with Transformers:

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

pipe = pipeline("text-generation", model="RinggAI/Transcript-Analytics-SLM0.5b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RinggAI/Transcript-Analytics-SLM0.5b")
model = AutoModelForCausalLM.from_pretrained("RinggAI/Transcript-Analytics-SLM0.5b")
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 RinggAI/Transcript-Analytics-SLM0.5b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RinggAI/Transcript-Analytics-SLM0.5b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RinggAI/Transcript-Analytics-SLM0.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RinggAI/Transcript-Analytics-SLM0.5b

SGLang

How to use RinggAI/Transcript-Analytics-SLM0.5b 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 "RinggAI/Transcript-Analytics-SLM0.5b" \
    --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": "RinggAI/Transcript-Analytics-SLM0.5b",
		"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 "RinggAI/Transcript-Analytics-SLM0.5b" \
        --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": "RinggAI/Transcript-Analytics-SLM0.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use RinggAI/Transcript-Analytics-SLM0.5b with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for RinggAI/Transcript-Analytics-SLM0.5b to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for RinggAI/Transcript-Analytics-SLM0.5b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for RinggAI/Transcript-Analytics-SLM0.5b to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="RinggAI/Transcript-Analytics-SLM0.5b",
    max_seq_length=2048,
)

Docker Model Runner
How to use RinggAI/Transcript-Analytics-SLM0.5b with Docker Model Runner:
```
docker model run hf.co/RinggAI/Transcript-Analytics-SLM0.5b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

As calling operations scale, it becomes clear that dialing and talking is not enough. Even with a strong voice AI + telephony architecture, the real value shows up only when post-call actions are captured and executed in a robust, dependable and consistent way. Closing the loop matters more than just connecting the call.

To support that, we’re releasing our Hindi + English transcript analytics model tuned specifically for call transcripts:

You can plug it into your calling or voice AI stack to automatically extract:

•	Enum-based classifications (e.g., call outcome, intent, disposition)
•	Conversation summaries
•	Action items / follow-ups

It’s built to handle real-world Hindi, English, and mixed Hinglish calls, including noisy transcripts.

Finetuning Parameters:

rank = 64
lora_alpha = rank*2,
target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
SFTConfig(
        dataset_text_field = "prompt",
        per_device_train_batch_size = 32,
        gradient_accumulation_steps = 1, # Use GA to mimic batch size!
        warmup_steps = 5,
        num_train_epochs = 3,
        learning_rate = 2e-4,
        logging_steps = 50,
        optim = "adamw_8bit",
        weight_decay = 0.001,
        lr_scheduler_type = "linear",
        seed = SEED,
        report_to = "wandb",
        eval_strategy="steps",
        eval_steps=200,
    )
The model was finetuned on ~100,000 curated transcripts across different domanins and language preferences

Provide the below schema for best output:

response_schema = {
        "type": "object",
        "properties": {
            "key_points": {
                "type": "array",
                "items": {"type": "string"},
                "nullable": True,
            },
            "action_items": {
                "type": "array",
                "items": {"type": "string"},
                "nullable": True,
            },
            "summary": {"type": "string"},
            "classification": classification_schema,
        },
        "required": ["summary", "classification"],
}

Developed by: RinggAI
License: apache-2.0
Finetuned from model : unsloth/Qwen2.5-0.5B-Instruct
Parameter decision where made using Schulman, J., & Thinking Machines Lab. (2025).
LoRA Without Regret.
Thinking Machines Lab: Connectionism.
DOI: 10.64434/tml.20250929
Link: https://thinkingmachines.ai/blog/lora/