Text Generation
Transformers
Safetensors
English
cma
custom_code
causal-lm
small-language-model
base-model
byte-level
Instructions to use User01110/CMA-1M-Mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use User01110/CMA-1M-Mini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="User01110/CMA-1M-Mini", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("User01110/CMA-1M-Mini", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use User01110/CMA-1M-Mini with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "User01110/CMA-1M-Mini" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "User01110/CMA-1M-Mini", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/User01110/CMA-1M-Mini
- SGLang
How to use User01110/CMA-1M-Mini 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 "User01110/CMA-1M-Mini" \ --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": "User01110/CMA-1M-Mini", "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 "User01110/CMA-1M-Mini" \ --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": "User01110/CMA-1M-Mini", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use User01110/CMA-1M-Mini with Docker Model Runner:
docker model run hf.co/User01110/CMA-1M-Mini
File size: 1,348 Bytes
564ee09 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | {
"step": 12000,
"parameters": 958692,
"lm_eval_version": "0.4.12",
"evaluation_dtype": "bfloat16",
"softmax_dtype": "float32",
"evaluation_autocast": false,
"num_fewshot": 0,
"bos_prefix": true,
"leaderboard_accuracy": {
"arc_easy": 0.29292929292929293,
"arc_challenge": 0.2175767918088737,
"hellaswag": 0.29346743676558457,
"piqa": 0.5462459194776932
},
"lm_eval_metric_policy": "acc_norm when available, otherwise acc",
"selection_metric": "validation.normalized_bpb (lower is better)",
"arithmark_2.0": {
"acc": 0.2744,
"correct": 686,
"total": 2500,
"by_operator_count": {
"1": {
"acc": 0.2552,
"correct": 319,
"total": 1250
},
"2": {
"acc": 0.30666666666666664,
"correct": 230,
"total": 750
},
"3": {
"acc": 0.274,
"correct": 137,
"total": 500
}
}
},
"arc_average": 0.2552530423690833,
"open_slm_leaderboard_average": 0.34234159965309024,
"average_formula": "(hellaswag + mean(arc_easy, arc_challenge) + piqa + arithmark_2.0) / 4",
"validation": {
"loss": 1.1765458586014317,
"perplexity": 3.2431525260156,
"normalized_bpb": 1.6973968755827331,
"tokens": 1144831,
"normalized_utf8_bytes": 1144831,
"window": 2048,
"stride": 1024
}
} |