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The dataset generation failed
Error code: DatasetGenerationError
Exception: TypeError
Message: Couldn't cast array of type
struct<caveats_recommendations: struct<deployment_recommendations: struct<confidence: string, content: string>, known_limitations: struct<confidence: string, content: string>, monitoring_requirements: struct<confidence: string, content: string>, user_guidelines: struct<confidence: string, content: string>>, ethical_considerations: struct<consent: struct<confidence: string, content: string>, cultural_appropriation: struct<confidence: string, content: string>, dual_use_risks: struct<confidence: string, content: string>, economic_impact: struct<confidence: string, content: string>, environmental_impact: struct<confidence: string, content: string>, intellectual_property: struct<confidence: string, content: string>, misinformation: struct<confidence: string, content: string>, privacy: struct<confidence: string, content: string>>, generative_capabilities: struct<consistency: struct<confidence: string, content: string>, content_types: struct<confidence: string, content: string>, customization: struct<confidence: string, content: string>, generation_quality: struct<confidence: string, content: string>, latency: struct<confidence: string, content: string>, length_limitations: struct<confidence: string, content: string>>, intended_use: struct<age_restrictions: struct<confidence: string, content: string>, out_of_scope_uses: struct<confidence: string, content: string>, primary_applications: struct<confidence: string, content: string>, supported_languages_domains: struct<confidence: strin
...
tring, content: string>, model_size: struct<confidence: string, content: string>, training_methodology: struct<confidence: string, content: string>, version_information: struct<confidence: string, content: string>>, performance_metrics: struct<bias_metrics: struct<confidence: string, content: string>, cultural_sensitivity: struct<confidence: string, content: string>, factual_accuracy: struct<confidence: string, content: string>, generation_quality_metrics: struct<confidence: string, content: string>, robustness: struct<confidence: string, content: string>, safety_metrics: struct<confidence: string, content: string>>, raw_content: struct<confidence: string, content: string>, safety_considerations: struct<bias_analysis: struct<confidence: string, content: string>, child_safety: struct<confidence: string, content: string>, content_safety: struct<confidence: string, content: string>, fairness_metrics: struct<confidence: string, content: string>, jailbreaking_resistance: struct<confidence: string, content: string>, red_team_testing: struct<confidence: string, content: string>>, training_data: struct<consent_privacy: struct<confidence: string, content: string>, data_filtering: struct<confidence: string, content: string>, demographic_representation: struct<confidence: string, content: string>, evaluation_datasets: struct<confidence: string, content: string>, language_coverage: struct<confidence: string, content: string>, training_corpus: struct<confidence: string, content: string>>>
to
{'caveats_recommendations': {'deployment_recommendations': {'confidence': Value('string'), 'content': Value('string')}, 'known_limitations': {'confidence': Value('string'), 'content': Value('string')}, 'monitoring_requirements': {'confidence': Value('string'), 'content': Value('string')}, 'user_guidelines': {'confidence': Value('string'), 'content': Value('string')}}, 'ethical_considerations': {'consent': {'confidence': Value('string'), 'content': Value('string')}, 'cultural_appropriation': {'confidence': Value('string'), 'content': Value('string')}, 'dual_use_risks': {'confidence': Value('string'), 'content': Value('string')}, 'economic_impact': {'confidence': Value('string'), 'content': Value('string')}, 'environmental_impact': {'confidence': Value('string'), 'content': Value('string')}, 'intellectual_property': {'confidence': Value('string'), 'content': Value('string')}, 'misinformation': {'confidence': Value('string'), 'content': Value('string')}, 'privacy': {'confidence': Value('string'), 'content': Value('string')}}, 'generative_capabilities': {'consistency': {'confidence': Value('string'), 'content': Value('string')}, 'content_types': {'confidence': Value('string'), 'content': Value('string')}, 'customization': {'confidence': Value('string'), 'content': Value('string')}, 'generation_quality': {'confidence': Value('string'), 'content': Value('string')}, 'latency': {'confidence': Value('string'), 'content': Value('string')}, 'length_limitations': {'confidence': Value('st
...
: {'confidence': Value('string'), 'content': Value('string')}, 'cultural_sensitivity': {'confidence': Value('string'), 'content': Value('string')}, 'factual_accuracy': {'confidence': Value('string'), 'content': Value('string')}, 'generation_quality_metrics': {'confidence': Value('string'), 'content': Value('string')}, 'robustness': {'confidence': Value('string'), 'content': Value('string')}, 'safety_metrics': {'confidence': Value('string'), 'content': Value('string')}}, 'safety_considerations': {'bias_analysis': {'confidence': Value('string'), 'content': Value('string')}, 'child_safety': {'confidence': Value('string'), 'content': Value('string')}, 'content_safety': {'confidence': Value('string'), 'content': Value('string')}, 'fairness_metrics': {'confidence': Value('string'), 'content': Value('string')}, 'jailbreaking_resistance': {'confidence': Value('string'), 'content': Value('string')}, 'red_team_testing': {'confidence': Value('string'), 'content': Value('string')}}, 'training_data': {'consent_privacy': {'confidence': Value('string'), 'content': Value('string')}, 'data_filtering': {'confidence': Value('string'), 'content': Value('string')}, 'demographic_representation': {'confidence': Value('string'), 'content': Value('string')}, 'evaluation_datasets': {'confidence': Value('string'), 'content': Value('string')}, 'language_coverage': {'confidence': Value('string'), 'content': Value('string')}, 'training_corpus': {'confidence': Value('string'), 'content': Value('string')}}}
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1831, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 644, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2223, in cast_table_to_schema
arrays = [
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2224, in <listcomp>
cast_array_to_feature(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1795, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1795, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2092, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<caveats_recommendations: struct<deployment_recommendations: struct<confidence: string, content: string>, known_limitations: struct<confidence: string, content: string>, monitoring_requirements: struct<confidence: string, content: string>, user_guidelines: struct<confidence: string, content: string>>, ethical_considerations: struct<consent: struct<confidence: string, content: string>, cultural_appropriation: struct<confidence: string, content: string>, dual_use_risks: struct<confidence: string, content: string>, economic_impact: struct<confidence: string, content: string>, environmental_impact: struct<confidence: string, content: string>, intellectual_property: struct<confidence: string, content: string>, misinformation: struct<confidence: string, content: string>, privacy: struct<confidence: string, content: string>>, generative_capabilities: struct<consistency: struct<confidence: string, content: string>, content_types: struct<confidence: string, content: string>, customization: struct<confidence: string, content: string>, generation_quality: struct<confidence: string, content: string>, latency: struct<confidence: string, content: string>, length_limitations: struct<confidence: string, content: string>>, intended_use: struct<age_restrictions: struct<confidence: string, content: string>, out_of_scope_uses: struct<confidence: string, content: string>, primary_applications: struct<confidence: string, content: string>, supported_languages_domains: struct<confidence: strin
...
tring, content: string>, model_size: struct<confidence: string, content: string>, training_methodology: struct<confidence: string, content: string>, version_information: struct<confidence: string, content: string>>, performance_metrics: struct<bias_metrics: struct<confidence: string, content: string>, cultural_sensitivity: struct<confidence: string, content: string>, factual_accuracy: struct<confidence: string, content: string>, generation_quality_metrics: struct<confidence: string, content: string>, robustness: struct<confidence: string, content: string>, safety_metrics: struct<confidence: string, content: string>>, raw_content: struct<confidence: string, content: string>, safety_considerations: struct<bias_analysis: struct<confidence: string, content: string>, child_safety: struct<confidence: string, content: string>, content_safety: struct<confidence: string, content: string>, fairness_metrics: struct<confidence: string, content: string>, jailbreaking_resistance: struct<confidence: string, content: string>, red_team_testing: struct<confidence: string, content: string>>, training_data: struct<consent_privacy: struct<confidence: string, content: string>, data_filtering: struct<confidence: string, content: string>, demographic_representation: struct<confidence: string, content: string>, evaluation_datasets: struct<confidence: string, content: string>, language_coverage: struct<confidence: string, content: string>, training_corpus: struct<confidence: string, content: string>>>
to
{'caveats_recommendations': {'deployment_recommendations': {'confidence': Value('string'), 'content': Value('string')}, 'known_limitations': {'confidence': Value('string'), 'content': Value('string')}, 'monitoring_requirements': {'confidence': Value('string'), 'content': Value('string')}, 'user_guidelines': {'confidence': Value('string'), 'content': Value('string')}}, 'ethical_considerations': {'consent': {'confidence': Value('string'), 'content': Value('string')}, 'cultural_appropriation': {'confidence': Value('string'), 'content': Value('string')}, 'dual_use_risks': {'confidence': Value('string'), 'content': Value('string')}, 'economic_impact': {'confidence': Value('string'), 'content': Value('string')}, 'environmental_impact': {'confidence': Value('string'), 'content': Value('string')}, 'intellectual_property': {'confidence': Value('string'), 'content': Value('string')}, 'misinformation': {'confidence': Value('string'), 'content': Value('string')}, 'privacy': {'confidence': Value('string'), 'content': Value('string')}}, 'generative_capabilities': {'consistency': {'confidence': Value('string'), 'content': Value('string')}, 'content_types': {'confidence': Value('string'), 'content': Value('string')}, 'customization': {'confidence': Value('string'), 'content': Value('string')}, 'generation_quality': {'confidence': Value('string'), 'content': Value('string')}, 'latency': {'confidence': Value('string'), 'content': Value('string')}, 'length_limitations': {'confidence': Value('st
...
: {'confidence': Value('string'), 'content': Value('string')}, 'cultural_sensitivity': {'confidence': Value('string'), 'content': Value('string')}, 'factual_accuracy': {'confidence': Value('string'), 'content': Value('string')}, 'generation_quality_metrics': {'confidence': Value('string'), 'content': Value('string')}, 'robustness': {'confidence': Value('string'), 'content': Value('string')}, 'safety_metrics': {'confidence': Value('string'), 'content': Value('string')}}, 'safety_considerations': {'bias_analysis': {'confidence': Value('string'), 'content': Value('string')}, 'child_safety': {'confidence': Value('string'), 'content': Value('string')}, 'content_safety': {'confidence': Value('string'), 'content': Value('string')}, 'fairness_metrics': {'confidence': Value('string'), 'content': Value('string')}, 'jailbreaking_resistance': {'confidence': Value('string'), 'content': Value('string')}, 'red_team_testing': {'confidence': Value('string'), 'content': Value('string')}}, 'training_data': {'consent_privacy': {'confidence': Value('string'), 'content': Value('string')}, 'data_filtering': {'confidence': Value('string'), 'content': Value('string')}, 'demographic_representation': {'confidence': Value('string'), 'content': Value('string')}, 'evaluation_datasets': {'confidence': Value('string'), 'content': Value('string')}, 'language_coverage': {'confidence': Value('string'), 'content': Value('string')}, 'training_corpus': {'confidence': Value('string'), 'content': Value('string')}}}
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1456, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1055, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 894, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 970, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1702, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1858, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
model_id string | generated_model_card dict |
|---|---|
QuantFactory/CursorCore-QW2.5-7B-GGUF | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "medium",
"content": "Use GGUF quantized weights with llama.cpp; pre-quantized GPTQ and AWQ artifacts are available; tag indicates endpoints compatibility"
},
"known_limitations": {
"confidence": "low",
"c... |
kothasuhas/1b-proposer-ctx16-5-8 | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "high",
"content": "Compatible with text-generation-inference and endpoints; model files available in safetensors format; tagged region: us"
},
"known_limitations": {
"confidence": "low",
"content": "Not p... |
FreedomIntelligence/HuatuoGPT-o1-8B | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "high",
"content": "Can be deployed with vllm or Sglang, or used for direct inference (as stated in usage)"
},
"known_limitations": {
"confidence": "low",
"content": "Not provided"
},
"monitoring_r... |
pawin205/Qwen-7B-Review-ICLR-GRPO-U | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "low",
"content": "Not provided"
},
"known_limitations": {
"confidence": "low",
"content": "Not provided"
},
"monitoring_requirements": {
"confidence": "low",
"content": "Not provided"
... |
openlm-research/open_llama_7b_v2 | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "high",
"content": "Use provided PyTorch/JAX checkpoints or EasyLM format for deployment; when loading with Hugging Face transformers, avoid the fast tokenizer (use use_fast=False or the provided tokenizer class); model is co... |
HuanjinYao/Mulberry_qwen2vl_7b | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "high",
"content": "Compatible with text-generation-inference and endpoints (tags indicate endpoints_compatible and text-generation-inference)"
},
"known_limitations": {
"confidence": "low",
"content": "No... |
juhw/q4104 | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "medium",
"content": "Model is tagged for endpoint deployment and region 'us' (inferred from tags 'endpoints_compatible' and 'region:us')"
},
"known_limitations": {
"confidence": "certain",
"content": "Not... |
QuantTrio/DeepSeek-R1-0528-GPTQ-Int4-Int8Mix-Lite | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "certain",
"content": "Choose variant based on available VRAM and desired quality: Lite (355 GB) for resource-constrained/lightweight server deployments; Compact (414 GB) for VRAM-sufficient deployments focused on answer qual... |
314e/mphctest-VLM-Gemma3-Entity | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "low",
"content": "Not provided"
},
"known_limitations": {
"confidence": "low",
"content": "Not provided"
},
"monitoring_requirements": {
"confidence": "low",
"content": "Not provided"
... |
llava-hf/vip-llava-13b-hf | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "low",
"content": "Not provided"
},
"known_limitations": {
"confidence": "low",
"content": "Not provided"
},
"monitoring_requirements": {
"confidence": "low",
"content": "Not provided"
... |
google/ul2 | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "high",
"content": "Given model size, examples note requirement of large GPU memory (example: at least a 40GB A100 GPU for example runs). Training used significant compute (pretraining over ~1 month and model parallelism of 8... |
cortexso/phi-3.5 | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "low",
"content": "Not provided"
},
"known_limitations": {
"confidence": "low",
"content": "Not provided"
},
"monitoring_requirements": {
"confidence": "low",
"content": "Not provided"
... |
mtgv/MobileVLM-1.7B | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "high",
"content": "Targeted for mobile deployment; inference examples available in the GitHub repository; built for off-the-shelf deployment using MobileLLaMA-1.4B-Chat."
},
"known_limitations": {
"confidence":... |
mav23/granite-8b-code-instruct-4k-GGUF | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "certain",
"content": "Perform safety testing and target-specific tuning before deploying in critical applications. Use few-shot examples to improve behavior on out-of-domain tasks."
},
"known_limitations": {
"c... |
Iscte-Sintra/Albertina-Kriolu | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "high",
"content": "Compatible with AutoTrain and endpoints deployment (inferred from tags 'autotrain_compatible' and 'endpoints_compatible')"
},
"known_limitations": {
"confidence": "low",
"content": "Not... |
pat-jj/text2graph-llama-3.2-3b | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "low",
"content": "Not provided"
},
"known_limitations": {
"confidence": "low",
"content": "Not provided"
},
"monitoring_requirements": {
"confidence": "low",
"content": "Not provided"
... |
atsuki-yamaguchi/Llama-2-7b-hf-de-30K-mean | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "low",
"content": "Not provided"
},
"known_limitations": {
"confidence": "low",
"content": "Not provided"
},
"monitoring_requirements": {
"confidence": "low",
"content": "Not provided"
... |
TheBloke/Llama-2-70B-AWQ | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "high",
"content": "Perform application-specific safety testing and tuning before deployment. Use vLLM for continuous batching/high-concurrency inference with AWQ models where applicable. Consider hardware sizing benefits of ... |
m-a-p/YuE-s1-7B-anneal-jp-kr-cot | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "high",
"content": "Use provided demos and tools (YuE-UI, Google Colab); quantized models and optimizations available to run on GPUs with ~8GB VRAM; dual-track ICL and incremental generation supported for flexible workflows"
... |
gerulata/slovakbert | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "certain",
"content": "Before tokenization, replace “ and ” with a single \" (double quote). Use the model for masked language modeling or fine-tune on downstream Slovak NLP tasks. The model can be used with a masked language... |
Qwen/Qwen3-1.7B | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "certain",
"content": "Refer to the project's blog, GitHub, and documentation for details on benchmark evaluation, hardware requirements, and inference performance."
},
"known_limitations": {
"confidence": "high... |
airev-ai/Amal-70b-v2 | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "low",
"content": "Not provided"
},
"known_limitations": {
"confidence": "low",
"content": "Not provided"
},
"monitoring_requirements": {
"confidence": "low",
"content": "Not provided"
... |
AQuarterMile/WritingBench-Critic-Model-Qwen-7B | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "high",
"content": "Frameworks used during training: Transformers 4.46.1, PyTorch 2.5.1+cu124, Datasets 3.1.0, Tokenizers 0.20.3. Tags indicate compatibility with text-generation-inference, autotrain_compatible, and endpoints... |
AIDX-ktds/ktdsbaseLM-v0.2-onbased-llama3.1 | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "certain",
"content": "Not provided"
},
"known_limitations": {
"confidence": "certain",
"content": "Not provided"
},
"monitoring_requirements": {
"confidence": "certain",
"content": "No... |
chujiezheng/Smaug-34B-v0.1-ExPO | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "low",
"content": "Not provided"
},
"known_limitations": {
"confidence": "low",
"content": "Not provided"
},
"monitoring_requirements": {
"confidence": "low",
"content": "Not provided"
... |
ucatalin1/llama-3.3-70B-robotics-sft-merged | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "certain",
"content": "Model distributed in safetensors format; compatible with 4-bit quantization (bitsandbytes); compatible with text-generation-inference and endpoints (tags: 'safetensors', '4-bit', 'bitsandbytes', 'text-g... |
answerdotai/ModernBERT-base | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "high",
"content": "Use with libraries starting from v4.48.0; supports PyTorch, ONNX, and safetensors formats; optimized for long-context inference via Unpadding and Flash Attention; autotrain compatibility indicated by tags"... |
RUCKBReasoning/TableLLM-13b | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "low",
"content": "Not provided"
},
"known_limitations": {
"confidence": "low",
"content": "Not provided"
},
"monitoring_requirements": {
"confidence": "low",
"content": "Not provided"
... |
nlpaueb/sec-bert-base | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "low",
"content": "Not provided"
},
"known_limitations": {
"confidence": "high",
"content": "Domain-specific to financial (SEC 10-K) text. Numeric tokenization motivated development of alternate variants (... |
bigscience/bloomz-560m | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "low",
"content": "Not provided"
},
"known_limitations": {
"confidence": "high",
"content": "Original pretrained BLOOM checkpoints are not recommended; some models finetuned on P3 were released for researc... |
TIGER-Lab/VL-Rethinker-7B | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "low",
"content": "Not provided"
},
"known_limitations": {
"confidence": "low",
"content": "Not provided"
},
"monitoring_requirements": {
"confidence": "low",
"content": "Not provided"
... |
MBZUAI/LaMini-GPT-774M | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "high",
"content": "Loadable via HuggingFace Transformers; PyTorch compatible; supports text-generation-inference and endpoints; AutoTrain compatible. Refer to the project repository and paper for dataset and usage details."
... |
scb10x/llama-3-typhoon-v1.5-8b-instruct | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "high",
"content": "Requires transformers 4.38.0 or newer; tags indicate compatibility with text-generation-inference and endpoints"
},
"known_limitations": {
"confidence": "low",
"content": "Not provided"... |
Xwin-LM/Xwin-Math-7B-V1.1 | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "low",
"content": "Not provided"
},
"known_limitations": {
"confidence": "high",
"content": "Evaluation results may vary by environment and hardware; authors note differences in evaluation strategy (strict... |
QuantFactory/Sailor2-L-8B-Chat-GGUF | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "low",
"content": "Not provided"
},
"known_limitations": {
"confidence": "low",
"content": "Not provided"
},
"monitoring_requirements": {
"confidence": "low",
"content": "Not provided"
... |
parler-tts/parler_tts_mini_v0.1 | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "low",
"content": "Not provided"
},
"known_limitations": {
"confidence": "low",
"content": "Not provided"
},
"monitoring_requirements": {
"confidence": "low",
"content": "Not provided"
... |
parler-tts/parler-tts-mini-v1 | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "low",
"content": "Not provided"
},
"known_limitations": {
"confidence": "low",
"content": "Not provided"
},
"monitoring_requirements": {
"confidence": "low",
"content": "Not provided"
... |
apple/OpenELM-1_1B-Instruct | {
"caveats_recommendations": {
"deployment_recommendations": {
"confidence": "low",
"content": "Not provided"
},
"known_limitations": {
"confidence": "low",
"content": "Not provided"
},
"monitoring_requirements": {
"confidence": "low",
"content": "Not provided"
... |
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