Text Generation
Transformers
Safetensors
Japanese
mixtral
Mixture of Experts
text-generation-inference
Instructions to use HachiML/youri-2x7b_dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HachiML/youri-2x7b_dev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="HachiML/youri-2x7b_dev")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("HachiML/youri-2x7b_dev") model = AutoModelForCausalLM.from_pretrained("HachiML/youri-2x7b_dev") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use HachiML/youri-2x7b_dev with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HachiML/youri-2x7b_dev" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HachiML/youri-2x7b_dev", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/HachiML/youri-2x7b_dev
- SGLang
How to use HachiML/youri-2x7b_dev 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 "HachiML/youri-2x7b_dev" \ --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": "HachiML/youri-2x7b_dev", "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 "HachiML/youri-2x7b_dev" \ --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": "HachiML/youri-2x7b_dev", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use HachiML/youri-2x7b_dev with Docker Model Runner:
docker model run hf.co/HachiML/youri-2x7b_dev
Upload 9 files
Browse files- benchmark_scores/result_jaqket_v2-0.2-0.2.json +24 -0
- benchmark_scores/result_jcola.json +26 -0
- benchmark_scores/result_jcommonsenseqa-1.1-0.2.1.json +26 -0
- benchmark_scores/result_jnli-1.3-0.2.json +30 -0
- benchmark_scores/result_jsquad-1.2-0.2.json +24 -0
- benchmark_scores/result_marc_ja-1.1-0.2.json +30 -0
- benchmark_scores/result_mgsm.json +24 -0
- benchmark_scores/result_xlsum_ja.json +23 -0
- benchmark_scores/result_xwinograd_ja.json +24 -0
benchmark_scores/result_jaqket_v2-0.2-0.2.json
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{
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"results": {
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"jaqket_v2-0.2-0.2": {
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"exact_match": 80.2405498281787,
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"f1": 84.5903685852139
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}
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},
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"versions": {
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"jaqket_v2-0.2-0.2": 0.2
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"config": {
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"model": "hf-causal-experimental",
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"model_args": "pretrained=HachiML/youri-2x7b_dev,tokenizer=HachiML/youri-2x7b_dev,use_accelerate=True,dtype=auto",
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"num_fewshot": [
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"batch_size": 2,
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"device": "cuda",
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"no_cache": false,
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"limit": null,
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"bootstrap_iters": 100000,
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"description_dict": null
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benchmark_scores/result_jcola.json
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{
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"results": {
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"jcola": {
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"balanced_acc": 0.5909437739064947,
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"mcc": 0.14568482514613612,
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"mcc_stderr": 0.03657851423629269,
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"macro_f1": 0.5553402665190399
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}
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"versions": {
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"jcola": 0.2
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"config": {
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"model": "hf-causal-experimental",
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"model_args": "pretrained=HachiML/youri-2x7b_dev,tokenizer=HachiML/youri-2x7b_dev,use_accelerate=True,dtype=auto",
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"num_fewshot": [
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"batch_size": 2,
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"device": "cuda",
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"no_cache": false,
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"limit": null,
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"bootstrap_iters": 100000,
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"description_dict": null
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benchmark_scores/result_jcommonsenseqa-1.1-0.2.1.json
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{
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"results": {
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"jcommonsenseqa-1.1-0.2.1": {
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"acc": 0.9115281501340483,
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"acc_stderr": 0.008493108942289001,
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"acc_norm": 0.837354781054513,
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"acc_norm_stderr": 0.011037087895481251
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"versions": {
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"jcommonsenseqa-1.1-0.2.1": 1.1
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},
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"config": {
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"model": "hf-causal-experimental",
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"model_args": "pretrained=HachiML/youri-2x7b_dev,tokenizer=HachiML/youri-2x7b_dev,use_accelerate=True,dtype=auto",
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"num_fewshot": [
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3
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"batch_size": 1,
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"device": "cuda",
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"no_cache": false,
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"limit": null,
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"bootstrap_iters": 100000,
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"description_dict": null
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benchmark_scores/result_jnli-1.3-0.2.json
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{
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"results": {
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"jnli-1.3-0.2": {
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"acc": 0.5866885784716516,
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"acc_stderr": 0.00998323894065562,
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"acc_norm": 0.5866885784716516,
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"acc_norm_stderr": 0.00998323894065562,
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"balanced_acc": 0.7103671626849409,
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"mcc": 0.48037594993558813,
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"mcc_stderr": 0.01202764597359472,
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"macro_f1": 0.6010291975400543
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}
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},
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"versions": {
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"jnli-1.3-0.2": 1.3
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},
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"config": {
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"model": "hf-causal-experimental",
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"model_args": "pretrained=HachiML/youri-2x7b_dev,tokenizer=HachiML/youri-2x7b_dev,use_accelerate=True,dtype=auto",
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"num_fewshot": [
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"batch_size": 8,
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"device": "cuda",
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"no_cache": false,
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"limit": null,
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"bootstrap_iters": 100000,
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"description_dict": null
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benchmark_scores/result_jsquad-1.2-0.2.json
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{
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"results": {
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"jsquad-1.2-0.2": {
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"exact_match": 80.07654209815398,
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"f1": 91.30275393110973
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}
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},
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"versions": {
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"jsquad-1.2-0.2": 1.2
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},
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"config": {
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"model": "hf-causal-experimental",
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"model_args": "pretrained=HachiML/youri-2x7b_dev,tokenizer=HachiML/youri-2x7b_dev,use_accelerate=True,dtype=auto",
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"num_fewshot": [
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2
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"batch_size": 1,
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"device": "cuda",
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"description_dict": null
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benchmark_scores/result_marc_ja-1.1-0.2.json
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{
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"results": {
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"marc_ja-1.1-0.2": {
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"acc": 0.9755925008843297,
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"acc_stderr": 0.0020523733894530468,
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"acc_norm": 0.9755925008843297,
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"acc_norm_stderr": 0.0020523733894530468,
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"balanced_acc": 0.958966027376291,
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"mcc": 0.9035669838271105,
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"mcc_stderr": 0.008066342268937397,
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"macro_f1": 0.9516669276635559
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}
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},
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"versions": {
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"marc_ja-1.1-0.2": 1.1
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},
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"config": {
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"model": "hf-causal-experimental",
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"model_args": "pretrained=HachiML/youri-2x7b_dev,tokenizer=HachiML/youri-2x7b_dev,use_accelerate=True,dtype=auto",
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"num_fewshot": [
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0
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],
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"batch_size": 2,
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"device": "cuda",
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"no_cache": false,
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"limit": null,
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"bootstrap_iters": 100000,
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"description_dict": null
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}
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}
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benchmark_scores/result_mgsm.json
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{
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"results": {
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"mgsm": {
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"acc": 0.248,
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"acc_stderr": 0.027367497504863555
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}
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},
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"versions": {
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"mgsm": 1.0
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},
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"config": {
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"model": "hf-causal-experimental",
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"model_args": "pretrained=HachiML/youri-2x7b_dev,tokenizer=HachiML/youri-2x7b_dev,use_accelerate=True,dtype=auto",
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"num_fewshot": [
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5
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],
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"batch_size": 2,
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"device": "cuda",
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"no_cache": false,
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"limit": null,
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"bootstrap_iters": 100000,
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"description_dict": null
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}
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}
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benchmark_scores/result_xlsum_ja.json
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{
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"results": {
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"xlsum_ja": {
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"rouge2": 25.628917796629892
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}
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},
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| 7 |
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"versions": {
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| 8 |
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"xlsum_ja": 1.0
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},
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| 10 |
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"config": {
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| 11 |
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"model": "hf-causal-experimental",
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"model_args": "pretrained=HachiML/youri-2x7b_dev,tokenizer=HachiML/youri-2x7b_dev,use_accelerate=True,dtype=auto",
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"num_fewshot": [
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1
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],
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"batch_size": 2,
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"device": "cuda",
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"no_cache": false,
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"limit": null,
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"bootstrap_iters": 100000,
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"description_dict": null
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}
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}
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benchmark_scores/result_xwinograd_ja.json
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{
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"results": {
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"xwinograd_ja": {
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"acc": 0.8143899895724713,
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"acc_stderr": 0.012561287517973916
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}
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},
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"versions": {
|
| 9 |
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| 10 |
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},
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| 11 |
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"config": {
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| 12 |
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"model": "hf-causal-experimental",
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| 13 |
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"model_args": "pretrained=HachiML/youri-2x7b_dev,tokenizer=HachiML/youri-2x7b_dev,use_accelerate=True,dtype=auto",
|
| 14 |
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| 15 |
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0
|
| 16 |
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],
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| 17 |
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"batch_size": 2,
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| 18 |
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"device": "cuda",
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| 19 |
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| 20 |
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| 21 |
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| 22 |
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| 23 |
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}
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| 24 |
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}
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