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The dataset generation failed
Error code: DatasetGenerationError
Exception: ArrowInvalid
Message: Float value 7.833 was truncated converting to int64
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 2086, in cast_array_to_feature
return array_cast(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1797, in wrapper
return func(array, *args, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1949, in array_cast
return array.cast(pa_type)
File "pyarrow/array.pxi", line 996, in pyarrow.lib.Array.cast
File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/compute.py", line 404, in cast
return call_function("cast", [arr], options, memory_pool)
File "pyarrow/_compute.pyx", line 590, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 385, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Float value 7.833 was truncated converting to int64
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 1438, 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
string | model_api_url
string | model_api_key
string | model_api_name
string | base_model
string | revision
string | precision
string | private
bool | weight_type
string | status
string | submitted_time
timestamp[us] | model_type
string | params
int64 | runsh
string | adapter
string | eval_id
int64 | flageval_id
int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Aria
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
Claude-3.5-Sonnet-20241022
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
Claude-3.7-Sonnet-20250219
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
Claude3-Opus-20240229
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
Doubao-Pro-Vision-32k-241028
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
GLM-4V-Plus
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
GPT-4o-20240806
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
GPT-4o-20241120
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
GPT-4o-mini-20240718
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
Gemini-1.5-Flash
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
Gemini-1.5-Pro
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
Gemini-2.0-Flash(experimental)
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
Gemini-2.0-pro-exp-20250205
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
Gemini-2.5-pro-preview-20250325
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
Gemma-3-27b-it
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
ICONNAI/ICONN-1-Mini-Beta
|
main
|
float16
| false |
Original
|
RUNNING
| 2025-06-24T18:53:39 |
🟢 : pretrained
| 7.833 | null | null |
||||||
Idefics3-8B-Llama3
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
InternVL2-2B
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
InternVL2-8B
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
InternVL2-Llama3-76B
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
InternVL2_5-26B
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
InternVL2_5-2B
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
InternVL2_5-8B
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
InternVL3-78B
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
InternVL3-8B
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
Janus-1.3B
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
LLaVA-OneVision-0.5B
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
LLaVA-OneVision-7B
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
LLaVA-Onevision-72B
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
Llama-3.2-11B-Vision-Instruct
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
Llama-3.2-90B-Vision-Instruct
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
Llama-4-maverick-instruct-basic
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
MiniCPM-V-2.6
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
Mistral-Small-3.1-24B-Instruct-2503
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
Molmo-72B-0924
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
Molmo-7B-D
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
Mono-InternVL-2B
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
NVLM-D-72B
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
Phi-3.5-Vision-Instruct
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
Phi-4-multimodal-instruct
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
Pixtral-12B-2409
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
Pixtral-Large-Instruct-2411
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
Qwen-VL-Max-20250402
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
Qwen-VL-Max
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
Qwen/Qwen2-VL-2B-Instruct
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T02:46:12 |
🟢 : pretrained
| 2.209 |
#!/bin/bash
current_file="$0"
current_dir="$(dirname "$current_file")"
SERVER_IP=$1
SERVER_PORT=$2
PYTHONPATH=$current_dir:$PYTHONPATH accelerate launch $current_dir/model_adapter.py --server_ip $SERVER_IP --server_port $SERVER_PORT "${@:3}" --cfg $current_dir/meta.json
|
import torch
from typing import Dict, Any
import time
from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from flagevalmm.server import ServerDataset
from flagevalmm.models.base_model_adapter import BaseModelAdapter
from flagevalmm.server.utils import parse_args, process_images_symbol
from qwen_vl_utils import process_vision_info
class CustomDataset(ServerDataset):
def __getitem__(self, index):
data = self.get_data(index)
question_id = data["question_id"]
img_path = data["img_path"]
qs = data["question"]
qs, idx = process_images_symbol(qs)
idx = set(idx)
img_path_idx = []
for i in idx:
if i < len(img_path):
img_path_idx.append(img_path[i])
else:
print("[warning] image index out of range")
return question_id, img_path_idx, qs
class ModelAdapter(BaseModelAdapter):
def model_init(self, task_info: Dict):
ckpt_path = task_info["model_path"]
torch.set_grad_enabled(False)
with self.accelerator.main_process_first():
tokenizer = AutoTokenizer.from_pretrained(ckpt_path, trust_remote_code=True)
model = Qwen2VLForConditionalGeneration.from_pretrained(
ckpt_path,
device_map="auto",
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2",
)
model = self.accelerator.prepare_model(model, evaluation_mode=True)
self.tokenizer = tokenizer
if hasattr(model, "module"):
model = model.module
self.model = model
self.processor = AutoProcessor.from_pretrained(ckpt_path)
def build_message(
self,
query: str,
image_paths=[],
) -> str:
messages = []
messages.append(
{
"role": "user",
"content": [],
},
)
for img_path in image_paths:
messages[-1]["content"].append(
{"type": "image", "image": img_path},
)
# add question
messages[-1]["content"].append(
{
"type": "text",
"text": query,
},
)
return messages
def run_one_task(self, task_name: str, meta_info: Dict[str, Any]):
results = []
cnt = 0
data_loader = self.create_data_loader(
CustomDataset, task_name, batch_size=1, num_workers=0
)
for question_id, img_path, qs in data_loader:
if cnt == 1:
start_time = time.perf_counter()
cnt += 1
question_id = question_id[0]
img_path_flaten = [p[0] for p in img_path]
qs = qs[0]
messages = self.build_message(qs, image_paths=img_path_flaten)
text = self.processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = self.processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to("cuda")
# Inference
generated_ids = self.model.generate(**inputs, max_new_tokens=1024)
generated_ids_trimmed = [
out_ids[len(in_ids) :]
for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
response = self.processor.batch_decode(
generated_ids_trimmed,
skip_special_tokens=True,
clean_up_tokenization_spaces=False,
)[0]
self.accelerator.print(f"{qs}\n{response}\n\n")
results.append(
{"question_id": question_id, "answer": response.strip(), "prompt": qs}
)
rank = self.accelerator.state.local_process_index
self.save_result(results, meta_info, rank=rank)
self.accelerator.wait_for_everyone()
if self.accelerator.is_main_process:
correct_num = self.collect_results_and_save(meta_info)
total_time = time.perf_counter() - start_time
print(
f"Total time: {total_time}\nAverage time:{total_time / cnt}\nResults_collect number: {correct_num}"
)
print("rank", rank, "finished")
if __name__ == "__main__":
args = parse_args()
model_adapter = ModelAdapter(
server_ip=args.server_ip,
server_port=args.server_port,
timeout=args.timeout,
extra_cfg=args.cfg,
)
model_adapter.run()
| 26,049 | 1,054 |
||||
Qwen2-VL-2B-Instruct
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
Qwen2-VL-72B-Instruct
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
Qwen2-VL-7B-Instruct
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
Qwen2.5-VL-32B-Instruct
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
Qwen2.5-VL-72B-Instruct
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
Qwen2.5-VL-7B-Instruct
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
Step-1V-32k
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
XGen-MM-Instruct-Interleave-v1.5
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
Yi-Vision
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | -1 | -1 |
||||||
deepseek-ai/Janus-Pro-7B
|
main
|
float16
| false |
Original
|
RUNNING
| 2025-02-14T06:58:30 |
🟢 : pretrained
| 0 |
#!/bin/bash
current_file="$0"
current_dir="$(dirname "$current_file")"
SERVER_IP=$1
SERVER_PORT=$2
cd /share/project/daiteng01/deepseek/Janus-main
pip install -e . -i http://10.1.1.16/repository/pypi-group/simple --trusted-host 10.1.1.16
cd -
PYTHONPATH=$current_dir:$PYTHONPATH accelerate launch $current_dir/model_adapter.py --server_ip $SERVER_IP --server_port $SERVER_PORT "${@:3}" --cfg $current_dir/meta.json
|
import time
from flagevalmm.server import ServerDataset
import sys
from flagevalmm.models.base_model_adapter import BaseModelAdapter
from flagevalmm.server.utils import (
parse_args,
default_collate_fn,
process_images_symbol,
load_pil_image,
)
from typing import Dict, Any
import torch
from transformers import AutoModelForCausalLM
from janus.models import MultiModalityCausalLM, VLChatProcessor
from janus.utils.io import load_pil_images
class CustomDataset(ServerDataset):
def __getitem__(self, index):
data = self.get_data(index)
qs, idx = process_images_symbol(
data["question"], dst_pattern="<image_placeholder>"
)
question_id = data["question_id"]
img_path = data["img_path"]
return question_id, qs, img_path
class ModelAdapter(BaseModelAdapter):
def model_init(self, task_info: Dict):
ckpt_path = task_info["model_path"]
torch.set_grad_enabled(False)
with self.accelerator.main_process_first():
self.vl_chat_processor: VLChatProcessor = VLChatProcessor.from_pretrained(ckpt_path)
self.tokenizer = self.vl_chat_processor.tokenizer
vl_gpt: MultiModalityCausalLM = AutoModelForCausalLM.from_pretrained(
ckpt_path, trust_remote_code=True
)
vl_gpt = vl_gpt.to(torch.bfloat16).cuda().eval()
model = self.accelerator.prepare_model(vl_gpt, evaluation_mode=True)
if hasattr(model, "module"):
model = model.module
self.model = model
def build_message(
self,
query: str,
image_paths=[],
) -> str:
content = ""
liang = len(image_paths) - query.count("<image_placeholder>")
print("= = shisha", query, len(image_paths), liang)
if liang < 0:
query = query.replace("<image_placeholder>", "", -liang)
else:
for i in range(liang):
content += "<image_placeholder>\n"
content += query
messages = [
{
"role": "<|User|>",
"content": content,
"images": image_paths,
},
{"role": "<|Assistant|>", "content": ""},
]
print("= = jieguo", messages, file=sys.stderr)
return messages
def run_one_task(self, task_name: str, meta_info: Dict[str, Any]):
results = []
cnt = 0
data_loader = self.create_data_loader(
CustomDataset,
task_name,
collate_fn=default_collate_fn,
batch_size=1,
num_workers=2,
)
for question_id, question, images in data_loader:
if cnt == 1:
start_time = time.perf_counter()
cnt += 1
messages = self.build_message(question[0], images[0])
pil_images = load_pil_images(messages)
prepare_inputs = self.vl_chat_processor(
conversations=messages, images=pil_images, force_batchify=True
).to(self.model.device)
inputs_embeds = self.model.prepare_inputs_embeds(**prepare_inputs)
# run the model to get the response
outputs = self.model.language_model.generate(
inputs_embeds=inputs_embeds,
attention_mask=prepare_inputs.attention_mask,
pad_token_id=self.tokenizer.eos_token_id,
bos_token_id=self.tokenizer.bos_token_id,
eos_token_id=self.tokenizer.eos_token_id,
max_new_tokens=4096,
do_sample=False,
use_cache=True,
)
response = self.tokenizer.decode(
outputs[0].cpu().tolist(), skip_special_tokens=True
)
self.accelerator.print(f"{question[0]}\n{response}\n\n")
results.append(
{
"question_id": question_id[0],
"answer": response.strip(),
"prompt": question[0],
}
)
rank = self.accelerator.state.local_process_index
# save results for the rank
self.save_result(results, meta_info, rank=rank)
self.accelerator.wait_for_everyone()
if self.accelerator.is_main_process:
correct_num = self.collect_results_and_save(meta_info)
total_time = time.perf_counter() - start_time
print(
f"Total time: {total_time}\nAverage time:{total_time / cnt}\nResults_collect number: {correct_num}"
)
print("rank", rank, "finished")
if __name__ == "__main__":
args = parse_args()
model_adapter = ModelAdapter(
server_ip=args.server_ip,
server_port=args.server_port,
timeout=args.timeout,
extra_cfg=args.cfg,
)
model_adapter.run()
| 27,651 | 1,083 |
||||
ernie-4.5-8k-preview-api
|
https://qianfan.baidubce.com/v2/chat/completions
|
bce-v3/ALTAK-81XtPT7TJ3qR0I8v3CRZV/e6e99c7b609f157ee4165696efb92ee920685e8f
|
ernie-4.5-8k-preview
|
main
|
float16
| false |
Original
|
CANCELLED
| 2025-03-24T10:03:27 |
🟢 : pretrained
| 0 | 26,280 | 1,077 |
|||
gemini-2.0-flash-thinking-exp-01-21
|
https://api.pandalla.ai/v1
|
sk-9XIHCjPZmZ23zqKuxQIxIE93cuo28QGH2sUqFtNWq6BuvMDE
|
gemini-2.0-flash-thinking-exp-01-21
|
main
|
float16
| false |
Original
|
CANCELLED
| 2025-02-27T02:28:02 |
🟢 : pretrained
| 0 | 26,276 | 1,072 |
|||
yi.daiteng01
|
https://api.lingyiwanwu.com/v1/chat/completions
|
876995f3b3ce41aca60b637fb51d752e
|
yi-vision
|
main
|
float16
| false |
Original
|
FINISHED
| 2025-01-24T07:22:04 |
🟢 : pretrained
| 0 | 26,055 | 1,055 |
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