Text Generation
Transformers
Safetensors
Sanskrit
sansar
sanskrit
devanagari
slp1
causal-lm
base-model
custom_code
Eval Results (legacy)
Instructions to use MuseMesh/sansar-700m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MuseMesh/sansar-700m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MuseMesh/sansar-700m", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("MuseMesh/sansar-700m", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MuseMesh/sansar-700m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MuseMesh/sansar-700m" # 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-700m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MuseMesh/sansar-700m
- SGLang
How to use MuseMesh/sansar-700m 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-700m" \ --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-700m", "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-700m" \ --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-700m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MuseMesh/sansar-700m with Docker Model Runner:
docker model run hf.co/MuseMesh/sansar-700m
Download eval/verification.json from MuseMesh/sansar-700m: direct link, hf CLI and curl.
- Browser
- Download file 4.74 kB
-
https://huggingface.co/MuseMesh/sansar-700m/resolve/main/eval/verification.json
- Command line
-
hf download hf://MuseMesh/sansar-700m/eval/verification.json
-
curl -L -o verification.json https://huggingface.co/MuseMesh/sansar-700m/resolve/main/eval/verification.json
4.74 kB
| { | |
| "size": "700m", | |
| "version": "v0.1.0", | |
| "run": "f10_slp1_uni8k_d700m_plus_all_v2_3x", | |
| "torch": "2.14.0+cu130", | |
| "transformers": "5.18.0", | |
| "hf_load_seconds": 0.4, | |
| "hf_param_counts": { | |
| "total": 704128512, | |
| "embedding": 12288000, | |
| "head": 12288000, | |
| "non_embedding": 679552512 | |
| }, | |
| "orig_param_counts": { | |
| "total": 704128512, | |
| "embedding": 12288000, | |
| "token_embedding": 12288000, | |
| "position_embedding": 0, | |
| "value_embedding": 0, | |
| "head": 12288000, | |
| "non_embedding": 679552512 | |
| }, | |
| "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": 0.10451602935791016, | |
| "mean_abs_diff_vs_fp32_master": 0.017697827890515327, | |
| "argmax_agree_vs_fp32_master": 1.0, | |
| "logit_abs_max": 13.280336380004883 | |
| }, | |
| { | |
| "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": 0.13284730911254883, | |
| "mean_abs_diff_vs_fp32_master": 0.0162887554615736, | |
| "argmax_agree_vs_fp32_master": 1.0, | |
| "logit_abs_max": 24.75530242919922 | |
| }, | |
| { | |
| "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": 0.15660560131072998, | |
| "mean_abs_diff_vs_fp32_master": 0.018970483914017677, | |
| "argmax_agree_vs_fp32_master": 0.99609375, | |
| "logit_abs_max": 26.321086883544922 | |
| }, | |
| { | |
| "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": 0.13342761993408203, | |
| "mean_abs_diff_vs_fp32_master": 0.016097350046038628, | |
| "argmax_agree_vs_fp32_master": 1.0, | |
| "logit_abs_max": 17.176036834716797 | |
| }, | |
| { | |
| "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": 0.10383224487304688, | |
| "mean_abs_diff_vs_fp32_master": 0.016606679186224937, | |
| "argmax_agree_vs_fp32_master": 1.0, | |
| "logit_abs_max": 17.724536895751953 | |
| }, | |
| { | |
| "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": 0.20844125747680664, | |
| "mean_abs_diff_vs_fp32_master": 0.013787541538476944, | |
| "argmax_agree_vs_fp32_master": 0.99609375, | |
| "logit_abs_max": 20.799428939819336 | |
| } | |
| ], | |
| "logits_max_abs_diff_impl": 0.0, | |
| "logits_max_abs_diff_bf16_storage": 0.20844125747680664, | |
| "left_padding_max_abs_diff": 3.528594970703125e-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": 0.48407, | |
| "dcs_gold": 0.66705, | |
| "gita": 0.10244, | |
| "ood": 0.49614, | |
| "prose": 0.48516, | |
| "vedic": 0.67513 | |
| }, | |
| "hf_bf16_weights_fp32_compute": { | |
| "pooled": 0.48405, | |
| "dcs_gold": 0.66683, | |
| "gita": 0.1024, | |
| "ood": 0.49612, | |
| "prose": 0.48514, | |
| "vedic": 0.6754 | |
| }, | |
| "pooled_rel_diff": -3.880216049873875e-05, | |
| "seconds": 104.1 | |
| }, | |
| "bpb_full": { | |
| "note": "every record, HF bf16 weights via transformers + trust_remote_code, extbench_bpb.py --fence_latin_false --no_bos --cfgs 512:256, CUDA bf16; ref = eval/bpb*.json", | |
| "std": { | |
| "hf_ex_gita": 0.53059, | |
| "ref_ex_gita": 0.53067, | |
| "rel_diff": -0.00016257954652671732, | |
| "hf": { | |
| "dcs_gold": 0.55111, | |
| "gita": 0.07577, | |
| "ood": 0.52853, | |
| "prose": 0.51859, | |
| "vedic": 0.66687 | |
| }, | |
| "ref": { | |
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| "gita": 0.07576, | |
| "ood": 0.52863, | |
| "prose": 0.5186, | |
| "vedic": 0.66705 | |
| }, | |
| "bytes_equal": true | |
| }, | |
| "clean_v1": { | |
| "hf_ex_gita": 0.55404, | |
| "ref_ex_gita": 0.55415, | |
| "rel_diff": -0.00019493170581936874, | |
| "hf": { | |
| "dcs_gold": 0.55453, | |
| "gita": 0.08184, | |
| "ood": 0.55967, | |
| "prose": 0.53144, | |
| "vedic": 0.66953 | |
| }, | |
| "ref": { | |
| "dcs_gold": 0.55472, | |
| "gita": 0.08182, | |
| "ood": 0.55979, | |
| "prose": 0.53148, | |
| "vedic": 0.66967 | |
| }, | |
| "bytes_equal": true | |
| } | |
| } | |
| } |