Instructions to use jc2375/transcript-to-note-mlx-6Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jc2375/transcript-to-note-mlx-6Bit with Transformers:

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

pipe = pipeline("image-text-to-text", model="jc2375/transcript-to-note-mlx-6Bit")
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("jc2375/transcript-to-note-mlx-6Bit")
model = AutoModelForImageTextToText.from_pretrained("jc2375/transcript-to-note-mlx-6Bit")
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]:]))

MLX

How to use jc2375/transcript-to-note-mlx-6Bit with MLX:

# Make sure mlx-vlm is installed
# pip install --upgrade mlx-vlm

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("jc2375/transcript-to-note-mlx-6Bit")
config = load_config("jc2375/transcript-to-note-mlx-6Bit")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps
LM Studio

vLLM

How to use jc2375/transcript-to-note-mlx-6Bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jc2375/transcript-to-note-mlx-6Bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jc2375/transcript-to-note-mlx-6Bit",
		"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/jc2375/transcript-to-note-mlx-6Bit

SGLang

How to use jc2375/transcript-to-note-mlx-6Bit 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 "jc2375/transcript-to-note-mlx-6Bit" \
    --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": "jc2375/transcript-to-note-mlx-6Bit",
		"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 "jc2375/transcript-to-note-mlx-6Bit" \
        --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": "jc2375/transcript-to-note-mlx-6Bit",
		"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"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use jc2375/transcript-to-note-mlx-6Bit with Docker Model Runner:
```
docker model run hf.co/jc2375/transcript-to-note-mlx-6Bit
```

transcript-to-note-mlx-6Bit

File size: 2,746 Bytes

e7ddb66

{
    "architectures": [
        "Gemma3ForConditionalGeneration"
    ],
    "boi_token_index": 255999,
    "bos_token_id": 2,
    "dtype": "float32",
    "eoi_token_index": 256000,
    "eos_token_id": [
        1,
        1,
        106
    ],
    "image_token_index": 262144,
    "initializer_range": 0.02,
    "mm_tokens_per_image": 256,
    "model_type": "gemma3",
    "pad_token_id": 0,
    "quantization": {
        "group_size": 64,
        "bits": 6,
        "mode": "affine"
    },
    "quantization_config": {
        "group_size": 64,
        "bits": 6,
        "mode": "affine"
    },
    "text_config": {
        "_sliding_window_pattern": 6,
        "attention_bias": false,
        "attention_dropout": 0.0,
        "attn_logit_softcapping": null,
        "dtype": "float32",
        "final_logit_softcapping": null,
        "head_dim": 256,
        "hidden_activation": "gelu_pytorch_tanh",
        "hidden_size": 2560,
        "initializer_range": 0.02,
        "intermediate_size": 10240,
        "layer_types": [
            "sliding_attention",
            "sliding_attention",
            "sliding_attention",
            "sliding_attention",
            "sliding_attention",
            "full_attention",
            "sliding_attention",
            "sliding_attention",
            "sliding_attention",
            "sliding_attention",
            "sliding_attention",
            "full_attention",
            "sliding_attention",
            "sliding_attention",
            "sliding_attention",
            "sliding_attention",
            "sliding_attention",
            "full_attention",
            "sliding_attention",
            "sliding_attention",
            "sliding_attention",
            "sliding_attention",
            "sliding_attention",
            "full_attention",
            "sliding_attention",
            "sliding_attention",
            "sliding_attention",
            "sliding_attention",
            "sliding_attention",
            "full_attention",
            "sliding_attention",
            "sliding_attention",
            "sliding_attention",
            "sliding_attention"
        ],
        "max_position_embeddings": 131072,
        "model_type": "gemma3_text",
        "num_attention_heads": 8,
        "num_hidden_layers": 34,
        "num_key_value_heads": 4,
        "query_pre_attn_scalar": 256,
        "rms_norm_eps": 1e-06,
        "rope_local_base_freq": 10000,
        "rope_scaling": {
            "factor": 8.0,
            "rope_type": "linear"
        },
        "rope_theta": 1000000,
        "sliding_window": 1024,
        "use_bidirectional_attention": false,
        "use_cache": true,
        "vocab_size": 262208
    },
    "transformers_version": "4.57.6"
}