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
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Download README.md from meihant/AnonymousModel-mht: direct link, hf CLI and curl.
- Browser
- Download file 1.43 kB
-
https://huggingface.co/meihant/AnonymousModel-mht/resolve/main/README.md
- Command line
-
hf download hf://meihant/AnonymousModel-mht/README.md
-
curl -L -o README.md https://huggingface.co/meihant/AnonymousModel-mht/resolve/main/README.md
1.43 kB
| library_name: transformers | |
| pipeline_tag: text-generation | |
| language: | |
| - en | |
| tags: | |
| - qwen3_5 | |
| - pretraining | |
| # AnonymousModel-mht | |
| Inference-only release of 20 budget-boundary checkpoints from two matched, | |
| randomly initialized Qwen3.5-4B text-model pretraining runs on approximately | |
| 20B body tokens. Each family contains ten approximately 2B-spaced checkpoints | |
| from 2B through 20B. The additional final checkpoints at approximately 20.156B | |
| tokens are intentionally excluded from this release. | |
| - [Keen-qwen4b-20b](Keen-qwen4b-20b/README.md): factual-list-conditioned pretraining. | |
| - [Vanilla-qwen4b-20b](Vanilla-qwen4b-20b/README.md): ordinary text pretraining. | |
| The training context was 4,096 tokens. These are base models, not QA-SFT or | |
| instruction-tuned models. Each checkpoint directory is independently loadable | |
| with standard Transformers 5.6.2 and contains BF16 safetensors weights, | |
| model/generation configuration and a tokenizer. Select the checkpoint using | |
| `subfolder`; this repository root is a collection and has no root model weights. | |
| Optimizer states, scheduler states and training logs are not distributed. | |
| Checkpoint loading examples and exact token counts appear in the family and | |
| checkpoint READMEs. `checkpoints.json` indexes the release. `manifest.json` | |
| records artifact hashes. Configuration enables inference caching; the released | |
| weights and tokenizer vocabulary are unchanged from their training checkpoints. | |