Instructions to use allenai/Flex-code-2x7B-1T with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use allenai/Flex-code-2x7B-1T with Transformers:

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

pipe = pipeline("text-generation", model="allenai/Flex-code-2x7B-1T")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("allenai/Flex-code-2x7B-1T")
model = AutoModelForCausalLM.from_pretrained("allenai/Flex-code-2x7B-1T", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use allenai/Flex-code-2x7B-1T with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "allenai/Flex-code-2x7B-1T"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "allenai/Flex-code-2x7B-1T",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/allenai/Flex-code-2x7B-1T

SGLang

How to use allenai/Flex-code-2x7B-1T 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 "allenai/Flex-code-2x7B-1T" \
    --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": "allenai/Flex-code-2x7B-1T",
		"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 "allenai/Flex-code-2x7B-1T" \
        --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": "allenai/Flex-code-2x7B-1T",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use allenai/Flex-code-2x7B-1T with Docker Model Runner:
```
docker model run hf.co/allenai/Flex-code-2x7B-1T
```

akshitab commited on Mar 2

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1 Parent(s): eb7c0e3

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@@ -58,8 +58,8 @@ print(tokenizer.decode(out[0]))
 | Reddit | 64.7 | 36.5 | 56.1 | 25.5 | 35.5 | 19.7 | 2.5 | 54.1 | 8.6 | 32.7 | 1.7 | 30.7 |
 | **Combined** |
 | BTM (top-2) | 68.7 | 57.7 | 59.4 | 28.3 | 43.2 | 44.3 | 23.1 | 73.6 | 54.4 | 46.3 | **24.0** | 47.6 |
-| 🔥 **FlexOlmo-7x7B-1T** | **70.4** | **60.1** | **60.2** | **30.5** | 44.8 | 46.8 | 47.9 | **78.3** | 66.2 | 53.8 | 14.6 | 52.0 |
-| **FlexOlmo-7x7B-1T-RT** | 70.3 | 60.0 | **60.2** | 30.3 | **45.2** | **47.2** | 47.7 | 77.2 | **67.6** | **53.9** | 13.3 | **52.2** |
 * The evaluation of the individual model refers to the dense model, not the 2x7B MoE model.

 | Reddit | 64.7 | 36.5 | 56.1 | 25.5 | 35.5 | 19.7 | 2.5 | 54.1 | 8.6 | 32.7 | 1.7 | 30.7 |
 | **Combined** |
 | BTM (top-2) | 68.7 | 57.7 | 59.4 | 28.3 | 43.2 | 44.3 | 23.1 | 73.6 | 54.4 | 46.3 | **24.0** | 47.6 |
+| **FlexOlmo-7x7B-1T** | 65.6 | 44.7 | 50.9 | 22.1 | 37.2 | 35.6 | 25.4 | 55.8 | 39.0 | 45.9 | 10.6 | 39.3 |
+| **FlexOlmo-7x7B-1T-RT** | **70.6** | **59.7** | **60.0** | **30.5** | **44.6** | **45.9** | 47.7 | **79.7** | **67.6** | **54.5** | 11.3 | **52.0** |
 * The evaluation of the individual model refers to the dense model, not the 2x7B MoE model.