Instructions to use Chat2Find/chat2find-instruct-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Chat2Find/chat2find-instruct-v1 with Transformers:

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

pipe = pipeline("text-generation", model="Chat2Find/chat2find-instruct-v1")
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("Chat2Find/chat2find-instruct-v1")
model = AutoModelForImageTextToText.from_pretrained("Chat2Find/chat2find-instruct-v1")
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 Chat2Find/chat2find-instruct-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Chat2Find/chat2find-instruct-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Chat2Find/chat2find-instruct-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Chat2Find/chat2find-instruct-v1

SGLang

How to use Chat2Find/chat2find-instruct-v1 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 "Chat2Find/chat2find-instruct-v1" \
    --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": "Chat2Find/chat2find-instruct-v1",
		"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 "Chat2Find/chat2find-instruct-v1" \
        --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": "Chat2Find/chat2find-instruct-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use Chat2Find/chat2find-instruct-v1 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 Chat2Find/chat2find-instruct-v1 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 Chat2Find/chat2find-instruct-v1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Chat2Find/chat2find-instruct-v1 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Chat2Find/chat2find-instruct-v1",
    max_seq_length=2048,
)

Docker Model Runner
How to use Chat2Find/chat2find-instruct-v1 with Docker Model Runner:
```
docker model run hf.co/Chat2Find/chat2find-instruct-v1
```

Avishka commited on 1 day ago

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@@ -21,7 +21,7 @@ pretty_name: Chat2Find Instruct v1 (7B Reasoning & Tool Model)
 Chat2Find-Instruct-v1 is a state-of-the-art, high-logic trilingual model optimized specifically for chain-of-thought (CoT) reasoning, agentic tool calling, and complex instruction-following in Sinhala, Tamil, and English.
-Based on our continued pre-trained model **Chat2Find-CPT** (which is built on the robust Qwen2.5-7B architecture) and fine-tuned using custom high-quality datasets through Unsloth, Chat2Find-Instruct-v1 is built to think before it speaks—allowing it to solve complex mathematical, logical, and agent-driven workflows seamlessly.
 ---
@@ -36,7 +36,7 @@ Based on our continued pre-trained model **Chat2Find-CPT** (which is built on th
 ## Technical Architecture & Training Details
-*   **Base Model**: [Chat2Find-CPT](https://huggingface.co/Chat2Find/Chat2Find-CPT) (Continued Pre-trained Model based on Qwen2.5-7B)
 *   **Maximum Sequence Length**: 262,144 tokens (Native Context Window)
 *   **Vocabulary**: Highly-optimized multilingual vocabulary supporting South Asian unicode blocks.

 Chat2Find-Instruct-v1 is a state-of-the-art, high-logic trilingual model optimized specifically for chain-of-thought (CoT) reasoning, agentic tool calling, and complex instruction-following in Sinhala, Tamil, and English.
+Based on our continued pre-trained model **Chat2Find-CPT** (which is built on the robust Qwen3.5-7B architecture) and fine-tuned using custom high-quality datasets through Unsloth, Chat2Find-Instruct-v1 is built to think before it speaks—allowing it to solve complex mathematical, logical, and agent-driven workflows seamlessly.
 ---
 ## Technical Architecture & Training Details
+*   **Base Model**: [Chat2Find-CPT](https://huggingface.co/Chat2Find/Chat2Find-CPT) (Continued Pre-trained Model based on Qwen3.5-7B)
 *   **Maximum Sequence Length**: 262,144 tokens (Native Context Window)
 *   **Vocabulary**: Highly-optimized multilingual vocabulary supporting South Asian unicode blocks.