Instructions to use RinggAI/Transcript-Analytics-Qwen3.5-0.8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RinggAI/Transcript-Analytics-Qwen3.5-0.8B with Transformers:

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

pipe = pipeline("image-text-to-text", model="RinggAI/Transcript-Analytics-Qwen3.5-0.8B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("RinggAI/Transcript-Analytics-Qwen3.5-0.8B")
model = AutoModelForImageTextToText.from_pretrained("RinggAI/Transcript-Analytics-Qwen3.5-0.8B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use RinggAI/Transcript-Analytics-Qwen3.5-0.8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RinggAI/Transcript-Analytics-Qwen3.5-0.8B"
# 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-Qwen3.5-0.8B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/RinggAI/Transcript-Analytics-Qwen3.5-0.8B

SGLang

How to use RinggAI/Transcript-Analytics-Qwen3.5-0.8B 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-Qwen3.5-0.8B" \
    --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-Qwen3.5-0.8B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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-Qwen3.5-0.8B" \
        --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-Qwen3.5-0.8B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Unsloth Studio new

How to use RinggAI/Transcript-Analytics-Qwen3.5-0.8B 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-Qwen3.5-0.8B 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-Qwen3.5-0.8B 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-Qwen3.5-0.8B to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="RinggAI/Transcript-Analytics-Qwen3.5-0.8B",
    max_seq_length=2048,
)

Docker Model Runner
How to use RinggAI/Transcript-Analytics-Qwen3.5-0.8B with Docker Model Runner:
```
docker model run hf.co/RinggAI/Transcript-Analytics-Qwen3.5-0.8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

The model was finetuned on ~76,000 curated transcripts across different domanins and language preferences

Expanded Training: Now optimized for CX Support, Healthcare, Loan Collection, Insurance, Ecommerce, and Concierge services.
Feature Improvement: Significantly enhanced relative date-time extraction for more precise data processing.
Training Overview
- 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
  - Relative DateTime Artifacts

It’s built to handle real-world Hindi, English, Indic noisy transcripts.

PS: VERY FEW EVALs WERE TAKEN FOR THE 0.8b MODEL

test out our even smatter SLM

Finetuning Parameters:

rank = 64 # kept small to know change inherent model intelligence but to make sure structured ectraction is followed
trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = train_dataset,
    eval_dataset  = test_dataset,
    args = SFTConfig(
        dataset_text_field = "prompt",
        max_seq_length = max_seq_length,
        per_device_train_batch_size = 5,   
        gradient_accumulation_steps = 5,   

        warmup_steps       = 10,           
        num_train_epochs   = 2,            
        learning_rate      = 2e-4,         
        lr_scheduler_type  = "linear",     

        optim        = "adamw_8bit",
        weight_decay = 0.01,               # Unsloth default (was 0.001)
        seed         = SEED,

        logging_steps  = 50,
        report_to      = "wandb",

        eval_strategy  = "steps",
        eval_steps     = 5000,
        save_strategy  = "steps",
        save_steps     = 5000,
        load_best_model_at_end  = True,    
        metric_for_best_model   = "eval_loss",

        output_dir     = "outputs_qwen35_0.8b",
        dataset_num_proc = 8,
        fp16= not torch.cuda.is_bf16_supported(),
        bf16=  torch.cuda.is_bf16_supported()
    ),
)

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,
            "callback_requested": {
                "type": "boolean",
                "nullable": False,
                "description": "If the user requested a callback or mentiones currently he is busy then value is true otherwise false",
            },
            "callback_requested_time": {
                "type": "string",
                "nullable": True,
                "description": "ISO 8601 datetime string (YYYY-MM-DDTHH:MM:SS) in the call's timezone, if user requested a callback",
            },
        },
        "required": ["summary", "classification"],
    }