Instructions to use microsoft/Fara1.5-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use microsoft/Fara1.5-27B with Transformers:

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

pipe = pipeline("image-text-to-text", model="microsoft/Fara1.5-27B")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("microsoft/Fara1.5-27B")
model = AutoModelForMultimodalLM.from_pretrained("microsoft/Fara1.5-27B", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use microsoft/Fara1.5-27B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "microsoft/Fara1.5-27B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "microsoft/Fara1.5-27B",
		"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/microsoft/Fara1.5-27B

SGLang

How to use microsoft/Fara1.5-27B 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 "microsoft/Fara1.5-27B" \
    --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": "microsoft/Fara1.5-27B",
		"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 "microsoft/Fara1.5-27B" \
        --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": "microsoft/Fara1.5-27B",
		"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 microsoft/Fara1.5-27B with Docker Model Runner:
```
docker model run hf.co/microsoft/Fara1.5-27B
```

Serving Fara with llama.cpp: use --reasoning-format none, or agent harnesses get empty content

by T0mSIlver - opened 4 days ago

Discussion

T0mSIlver

4 days ago

Hugging Face CLI

If you serve this model with llama-server and consume it from an agent harness (e.g. Magentic-UI's Fara web surfer), tool parsing can fail on every call, with the harness seeing an empty assistant message.

What happens: Fara writes its thoughts as plain text followed by a <tool_call>...</tool_call> block. It emits a <think> opening tag but never a closing </think>. With llama-server's default reasoning extraction (--reasoning-format unset/auto), the parser treats everything after <think> as reasoning — so the entire output (thoughts + tool call) lands in reasoning_content, and message.content comes back as "". Any client that reads only content (the OpenAI-standard field) gets an empty string. In Magentic-UI this surfaces as list index out of range. Retrying (1/3)... on every task.

Repro: identical chat completion request, same model (27B, llama.cpp b10107):

default reasoning format → content: "", thoughts + <tool_call> block in the reasoning field, finish_reason: stop
--reasoning-format none → full raw output in content, parses fine

Fix: launch llama-server with --reasoning-format none. If you have a router/proxy in front doing its own reasoning extraction, disable it there too. Note the raw output then starts with a stray unclosed <think> tag — harmless for parsers that split on <tool_call>, just cosmetic in displayed thoughts.

swhitehead

Microsoft org about 5 hours ago

Thank you for bringing this to our attention. Would you mind posting this as an issue on the Fara1.5 github repo so we can mention this in the hosting instructions? Thank you!

https://github.com/microsoft/fara

T0mSIlver

about 3 hours ago

Sure, here it is: https://github.com/microsoft/fara/issues/82
Thanks for the quick follow-up, and great work on Fara1.5, it's been really impressive to run locally.

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