Text Generation
Transformers
Safetensors
qwen2
llama-factory
full
Generated from Trainer
conversational
text-generation-inference
Instructions to use lllqaq/R2EGym-32B-Agent-Coder-R2egym-mid-post-step155 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lllqaq/R2EGym-32B-Agent-Coder-R2egym-mid-post-step155 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="lllqaq/R2EGym-32B-Agent-Coder-R2egym-mid-post-step155") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("lllqaq/R2EGym-32B-Agent-Coder-R2egym-mid-post-step155") model = AutoModelForCausalLM.from_pretrained("lllqaq/R2EGym-32B-Agent-Coder-R2egym-mid-post-step155") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use lllqaq/R2EGym-32B-Agent-Coder-R2egym-mid-post-step155 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lllqaq/R2EGym-32B-Agent-Coder-R2egym-mid-post-step155" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lllqaq/R2EGym-32B-Agent-Coder-R2egym-mid-post-step155", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/lllqaq/R2EGym-32B-Agent-Coder-R2egym-mid-post-step155
- SGLang
How to use lllqaq/R2EGym-32B-Agent-Coder-R2egym-mid-post-step155 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 "lllqaq/R2EGym-32B-Agent-Coder-R2egym-mid-post-step155" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lllqaq/R2EGym-32B-Agent-Coder-R2egym-mid-post-step155", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "lllqaq/R2EGym-32B-Agent-Coder-R2egym-mid-post-step155" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lllqaq/R2EGym-32B-Agent-Coder-R2egym-mid-post-step155", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use lllqaq/R2EGym-32B-Agent-Coder-R2egym-mid-post-step155 with Docker Model Runner:
docker model run hf.co/lllqaq/R2EGym-32B-Agent-Coder-R2egym-mid-post-step155
upload R2EGym 32B mid-post step155 final model (no ckpt)
Browse files- .gitattributes +1 -0
- README.md +57 -0
- added_tokens.json +24 -0
- all_results.json +8 -0
- chat_template.jinja +54 -0
- config.json +99 -0
- generation_config.json +13 -0
- merges.txt +0 -0
- model-00001-of-00014.safetensors +3 -0
- model-00002-of-00014.safetensors +3 -0
- model-00003-of-00014.safetensors +3 -0
- model-00004-of-00014.safetensors +3 -0
- model-00005-of-00014.safetensors +3 -0
- model-00006-of-00014.safetensors +3 -0
- model-00007-of-00014.safetensors +3 -0
- model-00008-of-00014.safetensors +3 -0
- model-00009-of-00014.safetensors +3 -0
- model-00010-of-00014.safetensors +3 -0
- model-00011-of-00014.safetensors +3 -0
- model-00012-of-00014.safetensors +3 -0
- model-00013-of-00014.safetensors +3 -0
- model-00014-of-00014.safetensors +3 -0
- model.safetensors.index.json +779 -0
- special_tokens_map.json +31 -0
- tokenizer.json +3 -0
- tokenizer_config.json +209 -0
- train_results.json +8 -0
- trainer_log.jsonl +55 -0
- trainer_state.json +421 -0
- training_args.bin +3 -0
- training_loss.png +0 -0
- vocab.json +0 -0
.gitattributes
CHANGED
|
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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README.md
ADDED
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@@ -0,0 +1,57 @@
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---
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library_name: transformers
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license: other
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tags:
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- llama-factory
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- full
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- generated_from_trainer
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model-index:
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- name: R2EGym-32B-Agent-Coder-R2egym-mid-post-step155
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results: []
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---
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# R2EGym-32B-Agent-Coder-R2egym-mid-post-step155
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This model is a fine-tuned version of an internal Buffer/sample10_final_step155 checkpoint on the r2egym_sft_trajectories dataset.
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 1
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- eval_batch_size: 8
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 6
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 12
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- total_eval_batch_size: 48
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 2.0
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### Training results
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### Framework versions
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- Transformers 4.55.0
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- Pytorch 2.8.0+cu128
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- Datasets 3.6.0
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- Tokenizers 0.21.1
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added_tokens.json
ADDED
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@@ -0,0 +1,24 @@
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{
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"</tool_call>": 151658,
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"<tool_call>": 151657,
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"<|box_end|>": 151649,
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"<|box_start|>": 151648,
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"<|endoftext|>": 151643,
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"<|file_sep|>": 151664,
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"<|fim_middle|>": 151660,
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"<|fim_pad|>": 151662,
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"<|fim_prefix|>": 151659,
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"<|fim_suffix|>": 151661,
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"<|im_end|>": 151645,
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"<|im_start|>": 151644,
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"<|image_pad|>": 151655,
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"<|object_ref_end|>": 151647,
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"<|object_ref_start|>": 151646,
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"<|quad_end|>": 151651,
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"<|quad_start|>": 151650,
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"<|repo_name|>": 151663,
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"<|video_pad|>": 151656,
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"<|vision_end|>": 151653,
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"<|vision_pad|>": 151654,
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"<|vision_start|>": 151652
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}
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all_results.json
ADDED
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{
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"epoch": 2.0,
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"total_flos": 2.1175585841610752e+17,
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"train_loss": 0.2914459398499242,
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"train_runtime": 58643.1461,
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"train_samples_per_second": 0.11,
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"train_steps_per_second": 0.009
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}
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chat_template.jinja
ADDED
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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| 18 |
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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+
{%- endif %}
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+
{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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| 23 |
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
|
| 24 |
+
{%- elif message.role == "assistant" %}
|
| 25 |
+
{{- '<|im_start|>' + message.role }}
|
| 26 |
+
{%- if message.content %}
|
| 27 |
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{{- '\n' + message.content }}
|
| 28 |
+
{%- endif %}
|
| 29 |
+
{%- for tool_call in message.tool_calls %}
|
| 30 |
+
{%- if tool_call.function is defined %}
|
| 31 |
+
{%- set tool_call = tool_call.function %}
|
| 32 |
+
{%- endif %}
|
| 33 |
+
{{- '\n<tool_call>\n{"name": "' }}
|
| 34 |
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{{- tool_call.name }}
|
| 35 |
+
{{- '", "arguments": ' }}
|
| 36 |
+
{{- tool_call.arguments | tojson }}
|
| 37 |
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{{- '}\n</tool_call>' }}
|
| 38 |
+
{%- endfor %}
|
| 39 |
+
{{- '<|im_end|>\n' }}
|
| 40 |
+
{%- elif message.role == "tool" %}
|
| 41 |
+
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
|
| 42 |
+
{{- '<|im_start|>user' }}
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| 43 |
+
{%- endif %}
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| 44 |
+
{{- '\n<tool_response>\n' }}
|
| 45 |
+
{{- message.content }}
|
| 46 |
+
{{- '\n</tool_response>' }}
|
| 47 |
+
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
| 48 |
+
{{- '<|im_end|>\n' }}
|
| 49 |
+
{%- endif %}
|
| 50 |
+
{%- endif %}
|
| 51 |
+
{%- endfor %}
|
| 52 |
+
{%- if add_generation_prompt %}
|
| 53 |
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{{- '<|im_start|>assistant\n' }}
|
| 54 |
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{%- endif %}
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config.json
ADDED
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{
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| 2 |
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"architectures": [
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| 3 |
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"Qwen2ForCausalLM"
|
| 4 |
+
],
|
| 5 |
+
"attention_dropout": 0.0,
|
| 6 |
+
"dtype": "bfloat16",
|
| 7 |
+
"eos_token_id": 151645,
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| 8 |
+
"hidden_act": "silu",
|
| 9 |
+
"hidden_size": 5120,
|
| 10 |
+
"initializer_range": 0.02,
|
| 11 |
+
"intermediate_size": 27648,
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| 12 |
+
"layer_types": [
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| 13 |
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"full_attention",
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| 14 |
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"full_attention",
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| 15 |
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"full_attention",
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| 16 |
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"full_attention",
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| 17 |
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"full_attention",
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| 18 |
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"full_attention",
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| 19 |
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"full_attention",
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| 20 |
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"full_attention",
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| 21 |
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"full_attention",
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| 22 |
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"full_attention",
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| 23 |
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"full_attention",
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| 24 |
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"full_attention",
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| 25 |
+
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|
| 26 |
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|
| 27 |
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| 28 |
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| 29 |
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| 30 |
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| 31 |
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| 32 |
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| 33 |
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| 34 |
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| 35 |
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| 36 |
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| 37 |
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| 38 |
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| 39 |
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| 40 |
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| 46 |
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| 47 |
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| 49 |
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| 50 |
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| 51 |
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| 52 |
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| 53 |
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| 54 |
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| 55 |
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| 56 |
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| 57 |
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| 60 |
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| 61 |
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| 64 |
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| 67 |
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generation_config.json
ADDED
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special_tokens_map.json
ADDED
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|
| 1 |
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{
|
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"additional_special_tokens": [
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|
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|
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|
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|
| 15 |
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|
| 16 |
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|
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|
| 18 |
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|
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|
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|
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|
| 22 |
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|
| 29 |
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|
| 30 |
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|
| 31 |
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tokenizer.json
ADDED
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@@ -0,0 +1,3 @@
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size 11421892
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tokenizer_config.json
ADDED
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|
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|
|
|
| 1 |
+
{
|
| 2 |
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|
| 3 |
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|
| 4 |
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|
| 5 |
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|
| 6 |
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|
| 7 |
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|
| 8 |
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|
| 9 |
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|
| 10 |
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|
| 11 |
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|
| 12 |
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|
| 13 |
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|
| 14 |
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|
| 15 |
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|
| 16 |
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|
| 17 |
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|
| 18 |
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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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|
| 24 |
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|
| 25 |
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|
| 26 |
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|
| 27 |
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|
| 28 |
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|
| 29 |
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|
| 30 |
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|
| 31 |
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|
| 32 |
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|
| 33 |
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|
| 34 |
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|
| 35 |
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|
| 36 |
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|
| 37 |
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|
| 38 |
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|
| 39 |
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|
| 40 |
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|
| 41 |
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|
| 42 |
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|
| 43 |
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|
| 44 |
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|
| 45 |
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|
| 46 |
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|
| 47 |
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|
| 48 |
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|
| 49 |
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|
| 50 |
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|
| 51 |
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| 52 |
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|
| 53 |
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| 54 |
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| 55 |
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| 56 |
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|
| 57 |
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|
| 58 |
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|
| 59 |
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| 60 |
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| 61 |
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| 62 |
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| 63 |
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| 64 |
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| 65 |
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|
| 66 |
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|
| 67 |
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| 68 |
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|
| 69 |
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| 70 |
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| 71 |
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| 72 |
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| 73 |
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| 74 |
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| 75 |
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| 76 |
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| 77 |
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| 78 |
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| 79 |
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| 80 |
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| 81 |
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| 82 |
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| 83 |
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| 84 |
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| 85 |
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| 86 |
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| 87 |
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| 88 |
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| 89 |
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| 90 |
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| 91 |
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| 92 |
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| 93 |
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| 94 |
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| 95 |
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| 96 |
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| 97 |
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| 98 |
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| 99 |
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| 100 |
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| 101 |
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| 102 |
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| 103 |
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| 104 |
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| 105 |
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| 106 |
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| 107 |
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| 108 |
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| 109 |
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| 110 |
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| 111 |
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| 112 |
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| 113 |
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| 114 |
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|
| 115 |
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| 116 |
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| 117 |
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| 118 |
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| 119 |
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| 120 |
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| 121 |
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| 122 |
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| 123 |
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| 124 |
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| 125 |
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| 126 |
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| 127 |
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| 128 |
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| 129 |
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| 130 |
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| 131 |
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| 132 |
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| 133 |
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| 134 |
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| 135 |
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| 136 |
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| 137 |
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| 138 |
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| 139 |
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| 140 |
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| 141 |
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| 142 |
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| 143 |
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| 144 |
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| 145 |
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| 146 |
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| 147 |
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| 148 |
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| 149 |
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| 151 |
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| 154 |
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| 155 |
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| 156 |
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| 157 |
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| 158 |
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| 161 |
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| 162 |
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| 163 |
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| 164 |
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| 165 |
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| 170 |
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| 171 |
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| 172 |
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| 173 |
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| 175 |
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| 177 |
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| 178 |
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| 179 |
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| 180 |
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| 181 |
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| 182 |
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| 183 |
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| 184 |
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| 185 |
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| 186 |
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| 187 |
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| 188 |
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| 189 |
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| 190 |
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| 191 |
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| 192 |
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|
| 193 |
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| 194 |
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| 195 |
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| 196 |
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| 197 |
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| 198 |
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|
| 199 |
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| 200 |
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| 201 |
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| 202 |
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|
| 203 |
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| 204 |
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|
| 205 |
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| 206 |
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|
| 207 |
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|
| 208 |
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|
| 209 |
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|
train_results.json
ADDED
|
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
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{
|
| 2 |
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| 3 |
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| 4 |
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| 5 |
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| 7 |
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| 8 |
+
}
|
trainer_log.jsonl
ADDED
|
@@ -0,0 +1,55 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{"current_steps": 10, "total_steps": 540, "loss": 3.1368, "lr": 1.6666666666666667e-06, "epoch": 0.03710575139146568, "percentage": 1.85, "elapsed_time": "0:22:25", "remaining_time": "19:48:13"}
|
| 2 |
+
{"current_steps": 20, "total_steps": 540, "loss": 1.9929, "lr": 3.5185185185185187e-06, "epoch": 0.07421150278293136, "percentage": 3.7, "elapsed_time": "0:40:58", "remaining_time": "17:45:27"}
|
| 3 |
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{"current_steps": 30, "total_steps": 540, "loss": 0.901, "lr": 5.370370370370371e-06, "epoch": 0.11131725417439703, "percentage": 5.56, "elapsed_time": "0:58:24", "remaining_time": "16:32:55"}
|
| 4 |
+
{"current_steps": 40, "total_steps": 540, "loss": 0.5934, "lr": 7.222222222222223e-06, "epoch": 0.14842300556586271, "percentage": 7.41, "elapsed_time": "1:16:04", "remaining_time": "15:50:59"}
|
| 5 |
+
{"current_steps": 50, "total_steps": 540, "loss": 0.396, "lr": 9.074074074074075e-06, "epoch": 0.18552875695732837, "percentage": 9.26, "elapsed_time": "1:32:41", "remaining_time": "15:08:18"}
|
| 6 |
+
{"current_steps": 60, "total_steps": 540, "loss": 0.3619, "lr": 9.997388623947927e-06, "epoch": 0.22263450834879406, "percentage": 11.11, "elapsed_time": "1:52:47", "remaining_time": "15:02:18"}
|
| 7 |
+
{"current_steps": 70, "total_steps": 540, "loss": 0.2826, "lr": 9.976513978965829e-06, "epoch": 0.2597402597402597, "percentage": 12.96, "elapsed_time": "2:10:34", "remaining_time": "14:36:40"}
|
| 8 |
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|
| 9 |
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|
| 10 |
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|
| 11 |
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|
| 12 |
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{"current_steps": 120, "total_steps": 540, "loss": 0.2282, "lr": 9.565094251780872e-06, "epoch": 0.4452690166975881, "percentage": 22.22, "elapsed_time": "3:39:29", "remaining_time": "12:48:14"}
|
| 13 |
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|
| 14 |
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|
| 15 |
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|
| 16 |
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|
| 17 |
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|
| 18 |
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|
| 19 |
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|
| 20 |
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{"current_steps": 200, "total_steps": 540, "loss": 0.2136, "lr": 7.95980540444038e-06, "epoch": 0.7421150278293135, "percentage": 37.04, "elapsed_time": "5:59:18", "remaining_time": "10:10:49"}
|
| 21 |
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{"current_steps": 210, "total_steps": 540, "loss": 0.2214, "lr": 7.69330932592594e-06, "epoch": 0.7792207792207793, "percentage": 38.89, "elapsed_time": "6:19:46", "remaining_time": "9:56:47"}
|
| 22 |
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|
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