Instructions to use KevalRx/molmo-pebble with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KevalRx/molmo-pebble with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="KevalRx/molmo-pebble", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("KevalRx/molmo-pebble", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use KevalRx/molmo-pebble with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "KevalRx/molmo-pebble" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "KevalRx/molmo-pebble", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/KevalRx/molmo-pebble
- SGLang
How to use KevalRx/molmo-pebble 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 "KevalRx/molmo-pebble" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "KevalRx/molmo-pebble", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "KevalRx/molmo-pebble" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "KevalRx/molmo-pebble", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use KevalRx/molmo-pebble with Docker Model Runner:
docker model run hf.co/KevalRx/molmo-pebble
| { | |
| "_name_or_path": "allenai/Molmo-7B-D-0924", | |
| "architectures": [ | |
| "MolmoForCausalLM" | |
| ], | |
| "attention_layer_norm": false, | |
| "auto_map": { | |
| "AutoConfig": "config_molmo.MolmoConfig", | |
| "AutoModelForCausalLM": "allenai/Molmo-7B-D-0924--modeling_molmo.MolmoForCausalLM" | |
| }, | |
| "clip_qkv": null, | |
| "embedding_size": 152064, | |
| "hidden_size": 3584, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 37888, | |
| "layer_norm_eps": 1e-06, | |
| "layer_norm_type": "rms", | |
| "max_position_embeddings": 4096, | |
| "model_type": "molmo", | |
| "norm_after": false, | |
| "num_attention_heads": 28, | |
| "num_hidden_layers": 28, | |
| "num_key_value_heads": 4, | |
| "qkv_bias": true, | |
| "quantization_config": { | |
| "_load_in_4bit": true, | |
| "_load_in_8bit": false, | |
| "bnb_4bit_compute_dtype": "bfloat16", | |
| "bnb_4bit_quant_storage": "uint8", | |
| "bnb_4bit_quant_type": "nf4", | |
| "bnb_4bit_use_double_quant": false, | |
| "llm_int8_enable_fp32_cpu_offload": false, | |
| "llm_int8_has_fp16_weight": false, | |
| "llm_int8_skip_modules": null, | |
| "llm_int8_threshold": 6.0, | |
| "load_in_4bit": true, | |
| "load_in_8bit": false, | |
| "quant_method": "bitsandbytes" | |
| }, | |
| "rope_theta": 1000000.0, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "float16", | |
| "transformers_version": "4.44.2", | |
| "use_cache": true, | |
| "use_position_ids": true, | |
| "vocab_size": 152064, | |
| "weight_tying": false | |
| } | |