Instructions to use humain-ai/ALLaM-7B-Instruct-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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)# pip install -U transformers accelerate # 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", 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
- 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
Tokenizer model_max_length is inconsistent with the documented 4096-token context
AutoTokenizer reports model_max_length=1000000000000000019884624838656, while config.max_position_embeddings=4096 and the model card documents a 4096-token context. Is this sentinel intentional, or should tokenizer_config.json expose 4096?
A proposed fix is now available in PR #10 and was verified on refs/pr/10: AutoTokenizer reports model_max_length=4096.
Additional behavioral proof: with a 6001-token Arabic input and truncation=True, main keeps all 6001 tokens, while refs/pr/10 truncates to 4096. This confirms the metadata difference changes AutoTokenizer truncation behavior. Proposed fix: PR #10.