Text Generation
Transformers
Safetensors
Sanskrit
sansar
sanskrit
devanagari
slp1
causal-lm
base-model
custom_code
Eval Results (legacy)
Instructions to use MuseMesh/sansar-60m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MuseMesh/sansar-60m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MuseMesh/sansar-60m", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("MuseMesh/sansar-60m", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MuseMesh/sansar-60m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MuseMesh/sansar-60m" # 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-60m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MuseMesh/sansar-60m
- SGLang
How to use MuseMesh/sansar-60m 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-60m" \ --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-60m", "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-60m" \ --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-60m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MuseMesh/sansar-60m with Docker Model Runner:
docker model run hf.co/MuseMesh/sansar-60m
Download eval/verification.json from MuseMesh/sansar-60m: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/MuseMesh/sansar-60m/resolve/main/eval/verification.json
- Command line
-
hf download hf://MuseMesh/sansar-60m/eval/verification.json
-
curl -L -o verification.json https://huggingface.co/MuseMesh/sansar-60m/resolve/main/eval/verification.json
3.58 kB
| { | |
| "size": "60m", | |
| "version": "v0.1.0", | |
| "run": "f0_slp1_uni8k_d60m", | |
| "torch": "2.14.0+cu130", | |
| "transformers": "5.18.0", | |
| "hf_load_seconds": 0.2, | |
| "hf_param_counts": { | |
| "total": 63173376, | |
| "embedding": 6537216, | |
| "head": 0, | |
| "non_embedding": 56636160 | |
| }, | |
| "orig_param_counts": { | |
| "total": 63173376, | |
| "embedding": 6537216, | |
| "token_embedding": 6144000, | |
| "position_embedding": 393216, | |
| "value_embedding": 0, | |
| "head": 0, | |
| "non_embedding": 56636160 | |
| }, | |
| "tied_head": true, | |
| "hf_head_is_tied": true, | |
| "logits": [ | |
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| "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": 0.08428049087524414, | |
| "mean_abs_diff_vs_fp32_master": 0.010810306295752525, | |
| "argmax_agree_vs_fp32_master": 1.0, | |
| "logit_abs_max": 15.046829223632812 | |
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| { | |
| "input": "random_512", | |
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| "mean_abs_diff_vs_same_bf16_weights": 0.0, | |
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| "logits_max_abs_diff_impl": 0.0, | |
| "logits_max_abs_diff_bf16_storage": 0.15094518661499023, | |
| "left_padding_max_abs_diff": 3.910064697265625e-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": 0.57871, | |
| "dcs_gold": 0.67942, | |
| "gita": 0.26099, | |
| "ood": 0.58167, | |
| "prose": 0.64115, | |
| "vedic": 1.11792 | |
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| "hf_bf16_weights_fp32_compute": { | |
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| "gita": 0.26107, | |
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| "pooled_rel_diff": 3.266520318733796e-05, | |
| "seconds": 11.0 | |
| } | |
| } |