Text Generation
Transformers
Safetensors
Chinese
English
ynet31
custom_code
ymodel
ymodel31
conversational
Instructions to use SnifferCaptain/YModel3.1-200M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SnifferCaptain/YModel3.1-200M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SnifferCaptain/YModel3.1-200M", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("SnifferCaptain/YModel3.1-200M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use SnifferCaptain/YModel3.1-200M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SnifferCaptain/YModel3.1-200M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SnifferCaptain/YModel3.1-200M", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/SnifferCaptain/YModel3.1-200M
- SGLang
How to use SnifferCaptain/YModel3.1-200M 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 "SnifferCaptain/YModel3.1-200M" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SnifferCaptain/YModel3.1-200M", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "SnifferCaptain/YModel3.1-200M" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SnifferCaptain/YModel3.1-200M", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use SnifferCaptain/YModel3.1-200M with Docker Model Runner:
docker model run hf.co/SnifferCaptain/YModel3.1-200M
File size: 2,492 Bytes
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"add_cross_attention": false,
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"architectures": [
"YForCausalLM31"
],
"finetuning_task": null,
"id2label": {
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"1": "LABEL_1"
},
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"LABEL_1": 1
},
"task_specific_params": null,
"problem_type": null,
"tokenizer_class": null,
"prefix": null,
"bos_token_id": 151644,
"pad_token_id": 151643,
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"num_heads": 8,
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"mla_qk_rope_head_dim": 64,
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"sengram_bucket_size": 8192,
"sengram_topk": 8,
"engram_bucket_size": 4096,
"engram_topk": 2,
"model_type": "ynet31",
"auto_map": {
"AutoConfig": "configuration_ymodel31.YConfig31",
"AutoModelForCausalLM": "modeling_ymodel31.YForCausalLM31"
}
} |