Instructions to use humain-ai/ALLaM-7B-Instruct-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use humain-ai/ALLaM-7B-Instruct-preview with Transformers:

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

pipe = pipeline("text-generation", model="humain-ai/ALLaM-7B-Instruct-preview")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("humain-ai/ALLaM-7B-Instruct-preview")
model = AutoModelForCausalLM.from_pretrained("humain-ai/ALLaM-7B-Instruct-preview")
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 humain-ai/ALLaM-7B-Instruct-preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "humain-ai/ALLaM-7B-Instruct-preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "humain-ai/ALLaM-7B-Instruct-preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/humain-ai/ALLaM-7B-Instruct-preview

SGLang

How to use humain-ai/ALLaM-7B-Instruct-preview 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 "humain-ai/ALLaM-7B-Instruct-preview" \
    --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": "humain-ai/ALLaM-7B-Instruct-preview",
		"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 "humain-ai/ALLaM-7B-Instruct-preview" \
        --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": "humain-ai/ALLaM-7B-Instruct-preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use humain-ai/ALLaM-7B-Instruct-preview with Docker Model Runner:
```
docker model run hf.co/humain-ai/ALLaM-7B-Instruct-preview
```

What are the hardware resources and requirements to host ALLaM-Instruct-preview-7B model?

by batman17 - opened Sep 10, 2025

Discussion

batman17

Sep 10, 2025

I'm looking to deploy and host the ALLaM-Instruct-preview-7B model and would appreciate guidance on the hardware requirements needed to run it effectively.

Could anyone share the recommended hardware resources, such as:

GPU: What is the minimum GPU VRAM required to run inference?
System RAM: How much RAM is needed to run the model efficiently?
Disk Space: How much storage space is necessary for model files and dependencies?
Other Requirements: Any additional hardware specs or optimizations?

Additionally, if anyone has experience running this model, I would love to hear about the setup and challenges faced during deployment.

Thanks in advance for your help!

AbdulmalekDS

Nov 18, 2025

for bare minimum usage so using a 4-bit or 8-bit quantized version I would say:
4–6 GB VRAM (e.g., RTX 2060/3050 or similar)

for (BF16/FP16):
14–16 GB VRAM (e.g., RTX 3090, 4090, A4000)

RAM

Minimum: 16 GB it would work on 11 GB (from my experiments)
Recommended: 32 GB

Disk Space

For storage, you mainly need space for the weights + environment:

Model Weights:
The weights and evaluation files located at: HuggingFace link

Typically require:
~14–16 GB for FP16 weights
~4–7 GB for quantized (4-bit/8-bit) weights

Environment & Dependencies:
PyTorch, Transformers, CUDA libs, runtime tools = ~5–8 GB

Total Recommended Disk Space:
20–25 GB (for FP16)
10–15 GB (for quantized)

Other Notes

use quantized versions and see if it fit your usecase, you can try to use Ollama also

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