Instructions to use lil-lab/CoLMLM-360M-FW with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lil-lab/CoLMLM-360M-FW with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="lil-lab/CoLMLM-360M-FW")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("lil-lab/CoLMLM-360M-FW") model = AutoModelForCausalLM.from_pretrained("lil-lab/CoLMLM-360M-FW", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use lil-lab/CoLMLM-360M-FW with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lil-lab/CoLMLM-360M-FW" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lil-lab/CoLMLM-360M-FW", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/lil-lab/CoLMLM-360M-FW
- SGLang
How to use lil-lab/CoLMLM-360M-FW 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 "lil-lab/CoLMLM-360M-FW" \ --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": "lil-lab/CoLMLM-360M-FW", "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 "lil-lab/CoLMLM-360M-FW" \ --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": "lil-lab/CoLMLM-360M-FW", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use lil-lab/CoLMLM-360M-FW with Docker Model Runner:
docker model run hf.co/lil-lab/CoLMLM-360M-FW
Add Co-LMLM 360M FineWeb-Edu retriever (inference weights + tokenizer)
Browse files- config.json +34 -0
- generation_config.json +9 -0
- model.safetensors +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +20 -0
config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 0,
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"dtype": "float32",
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"eos_token_id": 0,
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"head_dim": 64,
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"hidden_act": "silu",
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"hidden_size": 960,
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"initializer_range": 0.02,
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"intermediate_size": 2560,
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"is_llama_config": true,
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"max_position_embeddings": 8192,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 15,
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"num_hidden_layers": 32,
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"num_key_value_heads": 5,
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"pad_token_id": null,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_interleaved": false,
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"rope_parameters": {
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"rope_theta": 100000,
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"rope_type": "default"
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},
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"tie_word_embeddings": true,
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"transformers_version": "5.6.2",
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"use_cache": true,
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"vocab_size": 49155
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 0,
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"eos_token_id": 0,
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"output_attentions": false,
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"output_hidden_states": false,
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"transformers_version": "5.6.2",
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"use_cache": true
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:fc2e3c661341d1a609829f7ba0f95d69a0bb0585d2c00c5a132b245566bfdd46
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size 1447328600
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tokenizer.json
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": "<|endoftext|>",
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|endoftext|>",
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"errors": "replace",
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"extra_special_tokens": [
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"<FACT>",
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"</FACT>",
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"<FACT-q>"
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],
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"is_local": false,
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"local_files_only": false,
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"model_max_length": 8192,
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"pad_token": "<|endoftext|>",
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"tokenizer_class": "GPT2Tokenizer",
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"unk_token": "<|endoftext|>",
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"vocab_size": 49152
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}
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