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
```

Transcript-Analytics-SLM0.5b / README.md

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	---
	base_model: unsloth/Qwen2.5-0.5B-Instruct
	tags:
	- text-generation-inference
	- transformers
	- unsloth
	- qwen2
	license: apache-2.0
	language:
	- en
	---

	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
	```

	![Training Overview](metric_train_2.png)


	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/



	[<img style="border-radius: 20px;" src="https://storage.googleapis.com/desivocal-prod/desi-vocal/logo.png" width="200"/>](https://ringg.ai)
	[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)