Instructions to use meftah416/SmolLM-eppy-360m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use meftah416/SmolLM-eppy-360m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="meftah416/SmolLM-eppy-360m")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("meftah416/SmolLM-eppy-360m") model = AutoModelForCausalLM.from_pretrained("meftah416/SmolLM-eppy-360m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use meftah416/SmolLM-eppy-360m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "meftah416/SmolLM-eppy-360m" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "meftah416/SmolLM-eppy-360m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/meftah416/SmolLM-eppy-360m
- SGLang
How to use meftah416/SmolLM-eppy-360m 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 "meftah416/SmolLM-eppy-360m" \ --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": "meftah416/SmolLM-eppy-360m", "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 "meftah416/SmolLM-eppy-360m" \ --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": "meftah416/SmolLM-eppy-360m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Unsloth Desktop
- Docker Model Runner
How to use meftah416/SmolLM-eppy-360m with Docker Model Runner:
docker model run hf.co/meftah416/SmolLM-eppy-360m
Upload model trained with Unsloth
Browse filesUpload model trained with Unsloth 2x faster
- README.md +1 -0
- config.json +35 -0
- generation_config.json +10 -0
- model.safetensors +3 -0
README.md
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- unsloth
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- llama
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- trl
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license: apache-2.0
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language:
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- en
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- unsloth
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- llama
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- trl
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- sft
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license: apache-2.0
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language:
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- en
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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": "bfloat16",
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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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"max_position_embeddings": 4500,
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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": 16,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_parameters": {
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"factor": 2.197265625,
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"rope_theta": 10000.0,
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"rope_type": "linear",
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"type": "linear"
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},
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"tie_word_embeddings": true,
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"transformers_version": "5.5.0",
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"unsloth_version": "2026.5.10",
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"use_cache": false,
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"vocab_size": 49152
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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": [
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0
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],
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"max_length": 4500,
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"pad_token_id": 16,
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"transformers_version": "5.5.0"
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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 sha256:24908a6ef93e5da3a54bafee1354598c6070013acb1399a319cf21e7b71339fc
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size 723674912
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