Instructions to use RinggAI/Transcript-Analytics-SLM1.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RinggAI/Transcript-Analytics-SLM1.5b with Transformers:

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

pipe = pipeline("text-generation", model="RinggAI/Transcript-Analytics-SLM1.5b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RinggAI/Transcript-Analytics-SLM1.5b")
model = AutoModelForCausalLM.from_pretrained("RinggAI/Transcript-Analytics-SLM1.5b")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use RinggAI/Transcript-Analytics-SLM1.5b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RinggAI/Transcript-Analytics-SLM1.5b"
# 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-SLM1.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RinggAI/Transcript-Analytics-SLM1.5b

SGLang

How to use RinggAI/Transcript-Analytics-SLM1.5b 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-SLM1.5b" \
    --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-SLM1.5b",
		"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-SLM1.5b" \
        --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-SLM1.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use RinggAI/Transcript-Analytics-SLM1.5b 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-SLM1.5b 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-SLM1.5b 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-SLM1.5b to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="RinggAI/Transcript-Analytics-SLM1.5b",
    max_seq_length=2048,
)

Docker Model Runner
How to use RinggAI/Transcript-Analytics-SLM1.5b with Docker Model Runner:
```
docker model run hf.co/RinggAI/Transcript-Analytics-SLM1.5b
```

utkarshshukla2912 commited on Nov 26, 2025

Commit

2790428

verified ·

1 Parent(s): 8a5339d

Update README.md

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README.md +18 -3

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@@ -4,18 +4,33 @@ tags:
 - text-generation-inference
 - transformers
 - unsloth
-- qwen2
 license: apache-2.0
 language:
 - en
 ---
-# Uploaded finetuned  model
 - **Developed by:** RinggAI
 - **License:** apache-2.0
 - **Finetuned from model :** unsloth/Qwen2.5-1.5B-Instruct
-This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

 - text-generation-inference
 - transformers
 - unsloth
+- qwen2.5
 license: apache-2.0
 language:
 - en
+- hi
 ---
+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:
+🔗 Model: https://huggingface.co/RinggAI/Qwen2.5-1.5B-Instruct-Transcript-Analytics
+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.
 - **Developed by:** RinggAI
 - **License:** apache-2.0
 - **Finetuned from model :** unsloth/Qwen2.5-1.5B-Instruct
+[<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)