Text Generation
Transformers
Safetensors
Sanskrit
sansar
sanskrit
devanagari
slp1
causal-lm
base-model
custom_code
Eval Results (legacy)
Instructions to use MuseMesh/sansar-125m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MuseMesh/sansar-125m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MuseMesh/sansar-125m", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("MuseMesh/sansar-125m", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MuseMesh/sansar-125m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MuseMesh/sansar-125m" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MuseMesh/sansar-125m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MuseMesh/sansar-125m
- SGLang
How to use MuseMesh/sansar-125m 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 "MuseMesh/sansar-125m" \ --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": "MuseMesh/sansar-125m", "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 "MuseMesh/sansar-125m" \ --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": "MuseMesh/sansar-125m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MuseMesh/sansar-125m with Docker Model Runner:
docker model run hf.co/MuseMesh/sansar-125m
Download eval/verification.json from MuseMesh/sansar-125m: direct link, hf CLI and curl.
- Browser
- Download file 3.62 kB
-
https://huggingface.co/MuseMesh/sansar-125m/resolve/main/eval/verification.json
- Command line
-
hf download hf://MuseMesh/sansar-125m/eval/verification.json
-
curl -L -o verification.json https://huggingface.co/MuseMesh/sansar-125m/resolve/main/eval/verification.json
3.62 kB
| { | |
| "size": "125m", | |
| "version": "v0.1.0", | |
| "run": "f5_slp1_uni8k_d125m_plus_clean_2x", | |
| "torch": "2.14.0+cu130", | |
| "transformers": "5.18.0", | |
| "hf_load_seconds": 0.2, | |
| "hf_param_counts": { | |
| "total": 97241856, | |
| "embedding": 6144000, | |
| "head": 6144000, | |
| "non_embedding": 84953856 | |
| }, | |
| "orig_param_counts": { | |
| "total": 97241856, | |
| "embedding": 6144000, | |
| "token_embedding": 6144000, | |
| "position_embedding": 0, | |
| "value_embedding": 0, | |
| "head": 6144000, | |
| "non_embedding": 84953856 | |
| }, | |
| "tied_head": false, | |
| "hf_head_equals_wte": false, | |
| "logits": [ | |
| { | |
| "input": "dcs_gold", | |
| "tokens": 19, | |
| "max_abs_diff_vs_same_bf16_weights": 0.0, | |
| "mean_abs_diff_vs_same_bf16_weights": 0.0, | |
| "max_abs_diff_vs_fp32_master": 0.08556461334228516, | |
| "mean_abs_diff_vs_fp32_master": 0.012285877019166946, | |
| "argmax_agree_vs_fp32_master": 1.0, | |
| "logit_abs_max": 14.24844741821289 | |
| }, | |
| { | |
| "input": "gita", | |
| "tokens": 27, | |
| "max_abs_diff_vs_same_bf16_weights": 0.0, | |
| "mean_abs_diff_vs_same_bf16_weights": 0.0, | |
| "max_abs_diff_vs_fp32_master": 0.08663368225097656, | |
| "mean_abs_diff_vs_fp32_master": 0.013421257957816124, | |
| "argmax_agree_vs_fp32_master": 1.0, | |
| "logit_abs_max": 16.642593383789062 | |
| }, | |
| { | |
| "input": "ood", | |
| "tokens": 512, | |
| "max_abs_diff_vs_same_bf16_weights": 0.0, | |
| "mean_abs_diff_vs_same_bf16_weights": 0.0, | |
| "max_abs_diff_vs_fp32_master": 0.17093610763549805, | |
| "mean_abs_diff_vs_fp32_master": 0.014808709733188152, | |
| "argmax_agree_vs_fp32_master": 0.99609375, | |
| "logit_abs_max": 20.75664520263672 | |
| }, | |
| { | |
| "input": "prose", | |
| "tokens": 61, | |
| "max_abs_diff_vs_same_bf16_weights": 0.0, | |
| "mean_abs_diff_vs_same_bf16_weights": 0.0, | |
| "max_abs_diff_vs_fp32_master": 0.098907470703125, | |
| "mean_abs_diff_vs_fp32_master": 0.012143973261117935, | |
| "argmax_agree_vs_fp32_master": 1.0, | |
| "logit_abs_max": 18.785600662231445 | |
| }, | |
| { | |
| "input": "vedic", | |
| "tokens": 22, | |
| "max_abs_diff_vs_same_bf16_weights": 0.0, | |
| "mean_abs_diff_vs_same_bf16_weights": 0.0, | |
| "max_abs_diff_vs_fp32_master": 0.09095573425292969, | |
| "mean_abs_diff_vs_fp32_master": 0.013502920046448708, | |
| "argmax_agree_vs_fp32_master": 1.0, | |
| "logit_abs_max": 15.030223846435547 | |
| }, | |
| { | |
| "input": "random_512", | |
| "tokens": 512, | |
| "max_abs_diff_vs_same_bf16_weights": 0.0, | |
| "mean_abs_diff_vs_same_bf16_weights": 0.0, | |
| "max_abs_diff_vs_fp32_master": 0.18518781661987305, | |
| "mean_abs_diff_vs_fp32_master": 0.01284902635961771, | |
| "argmax_agree_vs_fp32_master": 0.99609375, | |
| "logit_abs_max": 23.490028381347656 | |
| } | |
| ], | |
| "logits_max_abs_diff_impl": 0.0, | |
| "logits_max_abs_diff_bf16_storage": 0.18518781661987305, | |
| "left_padding_max_abs_diff": 2.300739288330078e-05, | |
| "tokenizer_ids_identical": "95/100", | |
| "tokenizer_first_difference": { | |
| "set": "ood", | |
| "index": 2 | |
| }, | |
| "tokenizer_ids_identical_fence_latin_false": "100/100", | |
| "tokenizer_roundtrip_exact": "100/100", | |
| "bpb_sample": { | |
| "records_per_set": 20, | |
| "block": 512, | |
| "stride": 256, | |
| "original_fp32_master": { | |
| "pooled": 0.55835, | |
| "dcs_gold": 0.6511, | |
| "gita": 0.30412, | |
| "ood": 0.56483, | |
| "prose": 0.57064, | |
| "vedic": 0.79789 | |
| }, | |
| "hf_bf16_weights_fp32_compute": { | |
| "pooled": 0.55835, | |
| "dcs_gold": 0.65141, | |
| "gita": 0.30412, | |
| "ood": 0.56481, | |
| "prose": 0.57085, | |
| "vedic": 0.79782 | |
| }, | |
| "pooled_rel_diff": -1.4596361401117072e-05, | |
| "seconds": 25.4 | |
| } | |
| } |