Text Generation
Transformers
Safetensors
qwen2
llama-factory
full
Generated from Trainer
conversational
text-generation-inference
Instructions to use AgPerry/Qwen2.5-Coder-7B-Instruct-num06-accumulate_16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AgPerry/Qwen2.5-Coder-7B-Instruct-num06-accumulate_16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AgPerry/Qwen2.5-Coder-7B-Instruct-num06-accumulate_16") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AgPerry/Qwen2.5-Coder-7B-Instruct-num06-accumulate_16") model = AutoModelForCausalLM.from_pretrained("AgPerry/Qwen2.5-Coder-7B-Instruct-num06-accumulate_16") 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
- vLLM
How to use AgPerry/Qwen2.5-Coder-7B-Instruct-num06-accumulate_16 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AgPerry/Qwen2.5-Coder-7B-Instruct-num06-accumulate_16" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AgPerry/Qwen2.5-Coder-7B-Instruct-num06-accumulate_16", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/AgPerry/Qwen2.5-Coder-7B-Instruct-num06-accumulate_16
- SGLang
How to use AgPerry/Qwen2.5-Coder-7B-Instruct-num06-accumulate_16 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 "AgPerry/Qwen2.5-Coder-7B-Instruct-num06-accumulate_16" \ --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": "AgPerry/Qwen2.5-Coder-7B-Instruct-num06-accumulate_16", "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 "AgPerry/Qwen2.5-Coder-7B-Instruct-num06-accumulate_16" \ --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": "AgPerry/Qwen2.5-Coder-7B-Instruct-num06-accumulate_16", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use AgPerry/Qwen2.5-Coder-7B-Instruct-num06-accumulate_16 with Docker Model Runner:
docker model run hf.co/AgPerry/Qwen2.5-Coder-7B-Instruct-num06-accumulate_16
Upload folder using huggingface_hub
Browse files- .gitattributes +1 -0
- README.md +61 -0
- added_tokens.json +24 -0
- all_results.json +8 -0
- chat_template.jinja +54 -0
- config.json +58 -0
- generation_config.json +13 -0
- merges.txt +0 -0
- model-00001-of-00004.safetensors +3 -0
- model-00002-of-00004.safetensors +3 -0
- model-00003-of-00004.safetensors +3 -0
- model-00004-of-00004.safetensors +3 -0
- model.safetensors.index.json +347 -0
- special_tokens_map.json +31 -0
- tokenizer.json +3 -0
- tokenizer_config.json +208 -0
- train_results.json +8 -0
- trainer_log.jsonl +330 -0
- trainer_state.json +2346 -0
- training_args.bin +3 -0
- training_loss.png +0 -0
- vocab.json +0 -0
.gitattributes
CHANGED
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@@ -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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*.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
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---
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library_name: transformers
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license: other
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base_model: Qwen/Qwen2.5-Coder-7B-Instruct
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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: Qwen2.5-Coder-7B-Instruct-num06
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# Qwen2.5-Coder-7B-Instruct-num06
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This model is a fine-tuned version of [/mmu-vcg-hdd/multimodal/models/Qwen2.5-Coder-7B-Instruct](https://huggingface.co//mmu-vcg-hdd/multimodal/models/Qwen2.5-Coder-7B-Instruct) on the fim_midtrain_v2, the fim_midtrain_v3_pairs and the fim_midtrain_v3_triples datasets.
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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: 8
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- gradient_accumulation_steps: 16
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- total_train_batch_size: 128
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- total_eval_batch_size: 64
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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: 1.0
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### Training results
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### Framework versions
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- Transformers 4.57.6
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- Pytorch 2.10.0+cu128
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- Datasets 4.0.0
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- Tokenizers 0.22.2
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added_tokens.json
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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": 1.0,
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"total_flos": 8.530187773318005e+18,
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"train_loss": 0.4728999632081421,
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"train_runtime": 62323.8697,
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"train_samples_per_second": 6.767,
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"train_steps_per_second": 0.053
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}
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chat_template.jinja
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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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{{- '<|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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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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| 6 |
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"dtype": "bfloat16",
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| 7 |
+
"eos_token_id": 151645,
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| 8 |
+
"hidden_act": "silu",
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| 9 |
+
"hidden_size": 3584,
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| 10 |
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"initializer_range": 0.02,
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| 11 |
+
"intermediate_size": 18944,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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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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"full_attention",
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| 26 |
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"full_attention",
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| 27 |
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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| 39 |
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"full_attention",
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| 40 |
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"full_attention"
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],
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| 42 |
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"max_position_embeddings": 32768,
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| 43 |
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"max_window_layers": 28,
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| 44 |
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"model_type": "qwen2",
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| 45 |
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"num_attention_heads": 28,
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| 46 |
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"num_hidden_layers": 28,
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| 47 |
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"num_key_value_heads": 4,
|
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special_tokens_map.json
ADDED
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@@ -0,0 +1,31 @@
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{
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|
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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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tokenizer_config.json
ADDED
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@@ -0,0 +1,208 @@
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|
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|
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| 69 |
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| 70 |
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| 84 |
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| 86 |
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| 102 |
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| 126 |
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| 163 |
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| 164 |
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| 165 |
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| 171 |
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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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| 203 |
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| 206 |
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|
| 207 |
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|
| 208 |
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|
train_results.json
ADDED
|
@@ -0,0 +1,8 @@
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|
|
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|
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|
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|
|
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|
| 1 |
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{
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| 2 |
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"epoch": 1.0,
|
| 3 |
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"total_flos": 8.530187773318005e+18,
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| 4 |
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|
| 7 |
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| 8 |
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}
|
trainer_log.jsonl
ADDED
|
@@ -0,0 +1,330 @@
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|
| 1 |
+
{"current_steps": 10, "total_steps": 3295, "loss": 0.789, "lr": 2.7272727272727274e-07, "epoch": 0.0030350165029022344, "percentage": 0.3, "elapsed_time": "0:15:07", "remaining_time": "3 days, 10:47:43"}
|
| 2 |
+
{"current_steps": 20, "total_steps": 3295, "loss": 0.7904, "lr": 5.757575757575758e-07, "epoch": 0.006070033005804469, "percentage": 0.61, "elapsed_time": "0:29:14", "remaining_time": "3 days, 7:49:16"}
|
| 3 |
+
{"current_steps": 30, "total_steps": 3295, "loss": 0.744, "lr": 8.787878787878788e-07, "epoch": 0.009105049508706704, "percentage": 0.91, "elapsed_time": "0:43:09", "remaining_time": "3 days, 6:17:00"}
|
| 4 |
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{"current_steps": 40, "total_steps": 3295, "loss": 0.693, "lr": 1.181818181818182e-06, "epoch": 0.012140066011608937, "percentage": 1.21, "elapsed_time": "0:57:52", "remaining_time": "3 days, 6:28:56"}
|
| 5 |
+
{"current_steps": 50, "total_steps": 3295, "loss": 0.6656, "lr": 1.484848484848485e-06, "epoch": 0.015175082514511173, "percentage": 1.52, "elapsed_time": "1:05:27", "remaining_time": "2 days, 22:48:31"}
|
| 6 |
+
{"current_steps": 60, "total_steps": 3295, "loss": 0.6313, "lr": 1.787878787878788e-06, "epoch": 0.018210099017413408, "percentage": 1.82, "elapsed_time": "1:08:46", "remaining_time": "2 days, 13:47:58"}
|
| 7 |
+
{"current_steps": 70, "total_steps": 3295, "loss": 0.6098, "lr": 2.090909090909091e-06, "epoch": 0.021245115520315643, "percentage": 2.12, "elapsed_time": "1:11:52", "remaining_time": "2 days, 7:11:19"}
|
| 8 |
+
{"current_steps": 80, "total_steps": 3295, "loss": 0.5885, "lr": 2.393939393939394e-06, "epoch": 0.024280132023217875, "percentage": 2.43, "elapsed_time": "1:14:43", "remaining_time": "2 days, 2:03:12"}
|
| 9 |
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{"current_steps": 90, "total_steps": 3295, "loss": 0.5916, "lr": 2.6969696969696972e-06, "epoch": 0.02731514852612011, "percentage": 2.73, "elapsed_time": "1:18:02", "remaining_time": "1 day, 22:19:15"}
|
| 10 |
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{"current_steps": 100, "total_steps": 3295, "loss": 0.5753, "lr": 3e-06, "epoch": 0.030350165029022345, "percentage": 3.03, "elapsed_time": "1:20:42", "remaining_time": "1 day, 18:58:48"}
|
| 11 |
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{"current_steps": 110, "total_steps": 3295, "loss": 0.5597, "lr": 3.3030303030303033e-06, "epoch": 0.03338518153192458, "percentage": 3.34, "elapsed_time": "1:24:02", "remaining_time": "1 day, 16:33:19"}
|
| 12 |
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{"current_steps": 120, "total_steps": 3295, "loss": 0.5684, "lr": 3.606060606060606e-06, "epoch": 0.036420198034826816, "percentage": 3.64, "elapsed_time": "1:27:04", "remaining_time": "1 day, 14:23:39"}
|
| 13 |
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{"current_steps": 130, "total_steps": 3295, "loss": 0.5569, "lr": 3.90909090909091e-06, "epoch": 0.03945521453772905, "percentage": 3.95, "elapsed_time": "1:29:56", "remaining_time": "1 day, 12:29:46"}
|
| 14 |
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{"current_steps": 140, "total_steps": 3295, "loss": 0.5551, "lr": 4.212121212121212e-06, "epoch": 0.042490231040631286, "percentage": 4.25, "elapsed_time": "1:33:06", "remaining_time": "1 day, 10:58:24"}
|
| 15 |
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{"current_steps": 150, "total_steps": 3295, "loss": 0.5565, "lr": 4.5151515151515155e-06, "epoch": 0.045525247543533515, "percentage": 4.55, "elapsed_time": "1:35:49", "remaining_time": "1 day, 9:28:58"}
|
| 16 |
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{"current_steps": 160, "total_steps": 3295, "loss": 0.5482, "lr": 4.818181818181819e-06, "epoch": 0.04856026404643575, "percentage": 4.86, "elapsed_time": "1:39:06", "remaining_time": "1 day, 8:21:50"}
|
| 17 |
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{"current_steps": 170, "total_steps": 3295, "loss": 0.5482, "lr": 5.121212121212121e-06, "epoch": 0.051595280549337985, "percentage": 5.16, "elapsed_time": "1:42:05", "remaining_time": "1 day, 7:16:38"}
|
| 18 |
+
{"current_steps": 180, "total_steps": 3295, "loss": 0.5397, "lr": 5.424242424242425e-06, "epoch": 0.05463029705224022, "percentage": 5.46, "elapsed_time": "1:44:58", "remaining_time": "1 day, 6:16:47"}
|
| 19 |
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{"current_steps": 190, "total_steps": 3295, "loss": 0.5379, "lr": 5.727272727272728e-06, "epoch": 0.057665313555142456, "percentage": 5.77, "elapsed_time": "1:48:10", "remaining_time": "1 day, 5:27:41"}
|
| 20 |
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{"current_steps": 200, "total_steps": 3295, "loss": 0.5487, "lr": 6.030303030303031e-06, "epoch": 0.06070033005804469, "percentage": 6.07, "elapsed_time": "1:50:46", "remaining_time": "1 day, 4:34:15"}
|
| 21 |
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{"current_steps": 210, "total_steps": 3295, "loss": 0.5354, "lr": 6.333333333333333e-06, "epoch": 0.06373534656094693, "percentage": 6.37, "elapsed_time": "1:54:05", "remaining_time": "1 day, 3:55:59"}
|
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|
trainer_state.json
ADDED
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