Instructions to use meihant/AnonymousModel-mht with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use meihant/AnonymousModel-mht with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="meihant/AnonymousModel-mht")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("meihant/AnonymousModel-mht", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use meihant/AnonymousModel-mht with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "meihant/AnonymousModel-mht" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "meihant/AnonymousModel-mht", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/meihant/AnonymousModel-mht
- SGLang
How to use meihant/AnonymousModel-mht 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 "meihant/AnonymousModel-mht" \ --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": "meihant/AnonymousModel-mht", "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 "meihant/AnonymousModel-mht" \ --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": "meihant/AnonymousModel-mht", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use meihant/AnonymousModel-mht with Docker Model Runner:
docker model run hf.co/meihant/AnonymousModel-mht
Finalize verified models/KLLM-SmolLM2-360M-20B/checkpoints/14B
Browse files
models/KLLM-SmolLM2-360M-20B/checkpoints/14B/README.md
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# KLLM 14B model
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Artifacts for the SmolLM2-360M 20B comparison. Stored under `models/KLLM-SmolLM2-360M-20B/checkpoints/14B/` in `meihant/AnonymousModel-mht` (a model repository).
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{
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"step": 27916,
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"target_source_tokens": 14000000000,
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"source_tokens": 14000000003,
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"training_tokens": 14558730315,
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"checkpoint_kind": "evaluation",
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"resumable": false,
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"model": "SmolLM2-360M",
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"precision": "BF16 parameters and AdamW moments",
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"context": 4096,
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"global_batch": 128,
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"packing": "BFD",
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"learning_rate": 0.0005,
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"warmup_steps": 2000,
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"scheduler": "constant_with_warmup",
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"final_checkpoint": false
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}
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See manifest.json for artifact hashes and completeness. The original corpus is the shared nominal-20B FineWeb-Edu source. BFD uses 4096-token sequences and 1000-record candidate buffers.
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models/KLLM-SmolLM2-360M-20B/checkpoints/14B/manifest.json
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{
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"method": "kllm",
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"kind": "model",
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"step": 27916,
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"target_source_tokens": 14000000000,
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"source_tokens": 14000000003,
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"training_tokens": 14558730315,
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"checkpoint_kind": "evaluation",
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"resumable": false,
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"model": "SmolLM2-360M",
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"precision": "BF16 parameters and AdamW moments",
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"context": 4096,
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"global_batch": 128,
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"packing": "BFD",
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"learning_rate": 0.0005,
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"warmup_steps": 2000,
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"scheduler": "constant_with_warmup",
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"final_checkpoint": false,
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"files": [
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{
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"path": "config.json",
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"bytes": 778,
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"sha256": "d0321fb8700678e23f4cd206f7958d7488ac982f5097bb7dedce6f24197fb3fa"
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},
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{
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"path": "generation_config.json",
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"bytes": 194,
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"sha256": "b8733fba00d21edda23e6f7cca5cfe0312e5236dfa2b253e9f389f4ad0a48f58"
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},
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{
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"path": "model.safetensors",
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"bytes": 723893792,
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"sha256": "9f01d0c8682ea2c53fd9141435d2103cc61670338a913047c1b28fd90c93039f"
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},
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{
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"path": "tokenizer.json",
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"bytes": 3544733,
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"sha256": "f75fde26d0654775af4deb7f0cd0a4d94e7e92ce4b72ad4442aa9d352bc445f5"
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},
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{
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"path": "tokenizer_config.json",
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"bytes": 466,
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"sha256": "fd96339eaaad0449c7df04df9e38291e380418f7caa5157825d27da7a92b1751"
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},
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{
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"path": "training_progress.json",
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"bytes": 663,
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"sha256": "56b1add20d4c699bda9a178d8f9995d1d7ea1e4c12c9745eccf20be91514eb35"
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}
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]
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}
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