Instructions to use CrowdMind/Fred-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CrowdMind/Fred-9B with Transformers:

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

pipe = pipeline("image-text-to-text", model="CrowdMind/Fred-9B")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("CrowdMind/Fred-9B")
model = AutoModelForMultimodalLM.from_pretrained("CrowdMind/Fred-9B", device_map="auto")
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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CrowdMind/Fred-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CrowdMind/Fred-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CrowdMind/Fred-9B",
		"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/CrowdMind/Fred-9B

SGLang

How to use CrowdMind/Fred-9B 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 "CrowdMind/Fred-9B" \
    --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": "CrowdMind/Fred-9B",
		"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 "CrowdMind/Fred-9B" \
        --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": "CrowdMind/Fred-9B",
		"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 Desktop
Docker Model Runner
How to use CrowdMind/Fred-9B with Docker Model Runner:
```
docker model run hf.co/CrowdMind/Fred-9B
```

CrowdMind commited on Sep 11

Commit

71e26fc

verified ·

1 Parent(s): fce0767

Update README.md

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@@ -15,7 +15,7 @@ language:
 # Fred-9B
 **Fred-9B** is a fine-tuned 9B-parameter language model based on **Qwen
-3.5 9B**.
 Fred-9B was trained with **LoRA/QLoRA-style parameter-efficient
 fine-tuning** and is intended as an experimental conversational and
@@ -93,6 +93,8 @@ addition:
 2 + 6 + 3 = 11
 ```
 ### Example 2
 **Prompt**
@@ -110,6 +112,8 @@ The final answer is 15.
 The model breaks the expression down using standard order of operations
 and reaches the correct result.
 ## Intended Use
 Fred-9B is intended for:

 # Fred-9B
 **Fred-9B** is a fine-tuned 9B-parameter language model based on **Qwen
+3.5 9B** and finetunned with this dataset, https://huggingface.co/datasets/CrowdMind/caveman-fred.
 Fred-9B was trained with **LoRA/QLoRA-style parameter-efficient
 fine-tuning** and is intended as an experimental conversational and
 2 + 6 + 3 = 11
 ```
+![3](https://cdn-uploads.huggingface.co/production/uploads/66e629438ce4fc3270d9a910/jRRZRtqjcDQOd9lkCJKJ0.png)
 ### Example 2
 **Prompt**
 The model breaks the expression down using standard order of operations
 and reaches the correct result.
+![4](https://cdn-uploads.huggingface.co/production/uploads/66e629438ce4fc3270d9a910/eMQH7YwKhwjeyMiiduY6p.png)
 ## Intended Use
 Fred-9B is intended for: