Text Generation
Transformers
Safetensors
qwen2
Generated from Trainer
trl
grpo
conversational
text-generation-inference
Instructions to use RyanFan/RLVR-pmdata-sleep-quality with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RyanFan/RLVR-pmdata-sleep-quality with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="RyanFan/RLVR-pmdata-sleep-quality") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("RyanFan/RLVR-pmdata-sleep-quality") model = AutoModelForCausalLM.from_pretrained("RyanFan/RLVR-pmdata-sleep-quality", device_map="auto") 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use RyanFan/RLVR-pmdata-sleep-quality with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RyanFan/RLVR-pmdata-sleep-quality" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RyanFan/RLVR-pmdata-sleep-quality", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/RyanFan/RLVR-pmdata-sleep-quality
- SGLang
How to use RyanFan/RLVR-pmdata-sleep-quality 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 "RyanFan/RLVR-pmdata-sleep-quality" \ --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": "RyanFan/RLVR-pmdata-sleep-quality", "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 "RyanFan/RLVR-pmdata-sleep-quality" \ --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": "RyanFan/RLVR-pmdata-sleep-quality", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use RyanFan/RLVR-pmdata-sleep-quality with Docker Model Runner:
docker model run hf.co/RyanFan/RLVR-pmdata-sleep-quality
Model save
Browse files- README.md +3 -3
- chat_template.jinja +54 -0
- config.json +33 -4
- generation_config.json +1 -2
- model.safetensors +1 -1
- tokenizer_config.json +0 -1
- trainer_state.json +268 -213
- training_args.bin +2 -2
README.md
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## Training procedure
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/ryanzhang123-the-university-of-auckland/RLCR/runs/
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This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
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### Framework versions
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- TRL: 0.16.0.dev0
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- Transformers: 4.
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- Pytorch: 2.5.1
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- Datasets: 4.0.0
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- Tokenizers: 0.
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## Citations
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## Training procedure
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/ryanzhang123-the-university-of-auckland/RLCR/runs/an9do2om)
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This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
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### Framework versions
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- TRL: 0.16.0.dev0
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- Transformers: 4.57.6
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- Pytorch: 2.5.1
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- Datasets: 4.0.0
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- Tokenizers: 0.22.2
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## Citations
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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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"_name_or_path": "Qwen/Qwen2.5-1.5B-Instruct",
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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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-
"
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 1536,
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"initializer_range": 0.02,
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"intermediate_size": 8960,
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"max_position_embeddings": 32768,
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"max_window_layers": 21,
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"model_type": "qwen2",
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"num_attention_heads": 12,
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"num_hidden_layers": 28,
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"num_key_value_heads": 2,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 1000000.0,
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"sliding_window": null,
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"tie_word_embeddings": true,
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-
"
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-
"transformers_version": "4.48.3",
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"use_cache": false,
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"use_sliding_window": false,
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"vocab_size": 151936
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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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+
"dtype": "bfloat16",
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 1536,
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"initializer_range": 0.02,
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"intermediate_size": 8960,
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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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"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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"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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],
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"max_position_embeddings": 32768,
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"max_window_layers": 21,
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"model_type": "qwen2",
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"num_attention_heads": 12,
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"num_hidden_layers": 28,
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"num_key_value_heads": 2,
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+
"pad_token_id": 151643,
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"rms_norm_eps": 1e-06,
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| 50 |
"rope_scaling": null,
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"rope_theta": 1000000.0,
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| 52 |
"sliding_window": null,
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"tie_word_embeddings": true,
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+
"transformers_version": "4.57.6",
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"use_cache": false,
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"use_sliding_window": false,
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"vocab_size": 151936
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generation_config.json
CHANGED
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{
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-
"bos_token_id": 151643,
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"do_sample": true,
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"eos_token_id": [
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151645,
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@@ -10,5 +9,5 @@
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"temperature": 0.7,
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"top_k": 20,
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"top_p": 0.8,
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-
"transformers_version": "4.
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}
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{
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"do_sample": true,
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"eos_token_id": [
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151645,
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"temperature": 0.7,
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"top_k": 20,
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"top_p": 0.8,
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+
"transformers_version": "4.57.6"
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}
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model.safetensors
CHANGED
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@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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-
oid sha256:
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size 3554214752
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:7d1478beef101c30f89a0299831d5c5ad516e28ab13ce2f54cac15c86e320f02
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size 3554214752
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tokenizer_config.json
CHANGED
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@@ -195,7 +195,6 @@
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"<|video_pad|>"
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],
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"bos_token": null,
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-
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\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>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\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\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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"<|video_pad|>"
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],
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"bos_token": null,
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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trainer_state.json
CHANGED
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{
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"best_metric": null,
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"best_model_checkpoint": null,
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-
"epoch":
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-
"eval_steps":
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-
"global_step":
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"is_hyper_param_search": false,
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"is_local_process_zero": true,
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"is_world_process_zero": true,
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@@ -14,24 +15,24 @@
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"clip_ratio/low_mean": 0.0,
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"clip_ratio/low_min": 0.0,
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"clip_ratio/region_mean": 0.0,
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| 17 |
-
"completions/clipped_ratio": 0.
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| 18 |
-
"completions/max_length":
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-
"completions/max_terminated_length":
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-
"completions/mean_length":
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-
"completions/mean_terminated_length":
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-
"completions/min_length":
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"completions/min_terminated_length":
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-
"epoch": 0.
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-
"grad_norm": 0.
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"learning_rate": 1e-06,
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-
"loss": 0.
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-
"num_tokens":
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-
"reward": 0.
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-
"reward_std": 0.
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| 400 |
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| 401 |
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|
| 402 |
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| 403 |
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|
| 404 |
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|
| 405 |
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|
| 406 |
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|
| 407 |
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|
| 408 |
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"step": 69,
|
| 409 |
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|
| 410 |
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"train_loss": 0.005885362614998999,
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| 411 |
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"train_runtime": 1112.4293,
|
| 412 |
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|
| 413 |
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"train_steps_per_second": 0.062
|
| 414 |
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|
| 415 |
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|
| 416 |
"logging_steps": 5,
|
| 417 |
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"max_steps": 69,
|
| 418 |
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"num_input_tokens_seen": 4000983,
|
| 419 |
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"num_train_epochs": 1,
|
| 420 |
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"save_steps": 40,
|
| 421 |
"stateful_callbacks": {
|
| 422 |
"TrainerControl": {
|
| 423 |
"args": {
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|
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|
| 431 |
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|
| 432 |
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|
| 433 |
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|
| 434 |
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"train_batch_size": 4,
|
| 435 |
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|
| 436 |
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|
| 437 |
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|
training_args.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
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| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:d36c815be02d35da2ef1110a70e716729d767538210445941f93bed0f42a44c2
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| 3 |
+
size 8120
|