Instructions to use Interchained/imagine-v11 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Interchained/imagine-v11 with Transformers:

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

pipe = pipeline("text-generation", model="Interchained/imagine-v11")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Interchained/imagine-v11")
model = AutoModelForCausalLM.from_pretrained("Interchained/imagine-v11", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Interchained/imagine-v11 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Interchained/imagine-v11:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Interchained/imagine-v11:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Interchained/imagine-v11:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Interchained/imagine-v11:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Interchained/imagine-v11:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Interchained/imagine-v11:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Interchained/imagine-v11:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Interchained/imagine-v11:Q4_K_M

Use Docker

docker model run hf.co/Interchained/imagine-v11:Q4_K_M

LM Studio
Jan

vLLM

How to use Interchained/imagine-v11 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Interchained/imagine-v11"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Interchained/imagine-v11",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Interchained/imagine-v11:Q4_K_M

SGLang

How to use Interchained/imagine-v11 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 "Interchained/imagine-v11" \
    --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": "Interchained/imagine-v11",
		"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 "Interchained/imagine-v11" \
        --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": "Interchained/imagine-v11",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Interchained/imagine-v11 with Ollama:
```
ollama run hf.co/Interchained/imagine-v11:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use Interchained/imagine-v11 with Docker Model Runner:
```
docker model run hf.co/Interchained/imagine-v11:Q4_K_M
```

Lemonade

How to use Interchained/imagine-v11 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Interchained/imagine-v11:Q4_K_M

Run and chat with the model

lemonade run user.imagine-v11-Q4_K_M

List all available models

lemonade list

Atomic Chat

Interchained commited on 16 days ago

Commit

52ba540

verified ·

1 Parent(s): 86c2e48

Add imagine-v11 GGUF quants (part 1)

Browse files

Files changed (4) hide show

.gitattributes +3 -0
imagine-v11-Q4_0.gguf +3 -0
imagine-v11-Q4_K_M.gguf +3 -0
imagine-v11-Q5_K_M.gguf +3 -0

.gitattributes CHANGED Viewed

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text

 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+imagine-v11-Q4_0.gguf filter=lfs diff=lfs merge=lfs -text
+imagine-v11-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+imagine-v11-Q5_K_M.gguf filter=lfs diff=lfs merge=lfs -text

imagine-v11-Q4_0.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:eb6556110284eba400c2bb8eefb0469ff8cb60d76e0b4a36b3fbd22e280c7a09
+size 775935840

imagine-v11-Q4_K_M.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:5ab8710bba238f1ec1edfdf75aa763ee40670b4f3b9bd770f2095998b2ab4868
+size 873453408

imagine-v11-Q5_K_M.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:ad5eba966c5e5aa7d52d31705563ef5eff9a100b622843a845b4fd9cc4697a62
+size 1001838432