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 27, 2025

Commit

f840a48

verified ·

1 Parent(s): 60b9e9b

Update README.md

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README.md +31 -0

README.md CHANGED Viewed

@@ -25,10 +25,41 @@ You can plug it into your calling or voice AI stack to automatically extract:
 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

 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 = 2,
+        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](metrics.png)
 - **Developed by:** RinggAI
 - **License:** apache-2.0
 - **Finetuned from model :** unsloth/Qwen2.5-1.5B-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/