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README.md

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+---
+base_model: Qwen/Qwen2___5-3B-Instruct
+library_name: peft
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.15.2

adapter_config.json

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+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "Qwen/Qwen2___5-3B-Instruct",
+  "bias": "none",
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+  "eva_config": null,
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+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 32,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": [],
+  "peft_type": "LORA",
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
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+    "up_proj",
+    "v_proj",
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+    "q_proj",
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+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_rslora": false
+}

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additional_config.json

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+{"lora_dtype": null, "lorap_lr_ratio": null, "lorap_emb_lr": 1e-06}

args.json

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+{
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+  "model_type": "qwen2_5",
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+  "task_type": "causal_lm",
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+  "max_memory": {},
+  "local_repo_path": null,
+  "template": "qwen2_5",
+  "system": "You are a helpful assistant.",
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+  "padding_side": "right",
+  "loss_scale": "default",
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+  "use_chat_template": true,
+  "template_backend": "swift",
+  "dataset": [
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+    "AI-ModelScope/alpaca-gpt4-data-en#500",
+    "swift/self-cognition#500"
+  ],
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+  "data_seed": 42,
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+  "columns": {},
+  "strict": false,
+  "remove_unused_columns": true,
+  "model_name": [
+    "Chenhao"
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+  "model_author": [
+    "Chinamobile"
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Model(ms_model_id='Qwen/Qwen2.5-Coder-32B-Instruct-GPTQ-Int4', hf_model_id='Qwen/Qwen2.5-Coder-32B-Instruct-GPTQ-Int4', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-Coder-32B-Instruct-GPTQ-Int8', hf_model_id='Qwen/Qwen2.5-Coder-32B-Instruct-GPTQ-Int8', model_path=None, ms_revision=None, hf_revision=None)], ignore_patterns=None, requires=None, tags=['coding'])], template='qwen2_5', get_function=<function get_model_tokenizer_with_flash_attn at 0x7818d99dfe20>, model_arch='llama', architectures=['Qwen2ForCausalLM'], additional_saved_files=[], torch_dtype=None, is_multimodal=False, is_reward=False, task_type=None, ignore_patterns=['*.bin', '*.safetensors'], requires=['transformers>=4.37'], tags=[])",
+  "model_dir": "Qwen/Qwen2___5-3B-Instruct",
+  "hub": "<class 'swift.hub.hub.MSHub'>",
+  "evaluation_strategy": "steps",
+  "training_args": "Seq2SeqTrainingArguments(output_dir='/workspace/ms-swift/output/v0-20250416-154745', overwrite_output_dir=False, do_train=False, do_eval=True, do_predict=False, eval_strategy=<IntervalStrategy.STEPS: 'steps'>, prediction_loss_only=False, per_device_train_batch_size=1, per_device_eval_batch_size=1, per_gpu_train_batch_size=None, per_gpu_eval_batch_size=None, gradient_accumulation_steps=16, eval_accumulation_steps=None, eval_delay=0, torch_empty_cache_steps=None, learning_rate=0.0001, weight_decay=0.1, adam_beta1=0.9, adam_beta2=0.95, adam_epsilon=1e-08, max_grad_norm=1.0, num_train_epochs=1.0, max_steps=-1, lr_scheduler_type=<SchedulerType.COSINE: 'cosine'>, lr_scheduler_kwargs=None, warmup_ratio=0.05, warmup_steps=0, log_level='passive', log_level_replica='warning', log_on_each_node=True, logging_dir='/workspace/ms-swift/output/v0-20250416-154745/runs', logging_strategy=<IntervalStrategy.STEPS: 'steps'>, logging_first_step=True, logging_steps=5, logging_nan_inf_filter=True, save_strategy=<SaveStrategy.STEPS: 'steps'>, save_steps=50, save_total_limit=2, save_safetensors=True, save_on_each_node=False, save_only_model=False, restore_callback_states_from_checkpoint=False, no_cuda=False, use_cpu=False, use_mps_device=False, seed=42, data_seed=42, jit_mode_eval=False, use_ipex=False, bf16=True, fp16=False, fp16_opt_level='O1', half_precision_backend='auto', bf16_full_eval=False, fp16_full_eval=False, tf32=None, local_rank=0, ddp_backend=None, tpu_num_cores=None, tpu_metrics_debug=False, debug=[], dataloader_drop_last=False, eval_steps=50, dataloader_num_workers=4, dataloader_prefetch_factor=10, past_index=-1, run_name='/workspace/ms-swift/output/v0-20250416-154745', disable_tqdm=False, remove_unused_columns=False, label_names=None, load_best_model_at_end=False, metric_for_best_model='loss', greater_is_better=False, ignore_data_skip=False, fsdp=[], fsdp_min_num_params=0, fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}, tp_size=0, fsdp_transformer_layer_cls_to_wrap=None, accelerator_config=AcceleratorConfig(split_batches=False, dispatch_batches=False, even_batches=True, use_seedable_sampler=True, non_blocking=False, gradient_accumulation_kwargs=None, use_configured_state=False), deepspeed=None, label_smoothing_factor=0.0, optim=<OptimizerNames.ADAMW_TORCH: 'adamw_torch'>, optim_args=None, adafactor=False, group_by_length=False, length_column_name='length', report_to=['tensorboard'], ddp_find_unused_parameters=None, ddp_bucket_cap_mb=None, ddp_broadcast_buffers=None, dataloader_pin_memory=True, dataloader_persistent_workers=False, skip_memory_metrics=True, use_legacy_prediction_loop=False, push_to_hub=False, resume_from_checkpoint=None, hub_model_id=None, hub_strategy=<HubStrategy.EVERY_SAVE: 'every_save'>, hub_token=None, hub_private_repo=None, hub_always_push=False, gradient_checkpointing=True, gradient_checkpointing_kwargs=None, include_inputs_for_metrics=False, include_for_metrics=[], eval_do_concat_batches=True, fp16_backend='auto', push_to_hub_model_id=None, push_to_hub_organization=None, push_to_hub_token=None, mp_parameters='', auto_find_batch_size=False, full_determinism=False, torchdynamo=None, ray_scope='last', ddp_timeout=1800, torch_compile=False, torch_compile_backend=None, torch_compile_mode=None, include_tokens_per_second=None, include_num_input_tokens_seen=None, neftune_noise_alpha=None, optim_target_modules=None, batch_eval_metrics=False, eval_on_start=False, use_liger_kernel=False, eval_use_gather_object=False, average_tokens_across_devices=None, sortish_sampler=False, predict_with_generate=False, generation_max_length=None, generation_num_beams=None, generation_config=None, check_model=True, acc_strategy='token', train_dataloader_shuffle=True, metric_warmup_step=0, fsdp_num=1, acc_steps=1, eval_use_evalscope=False, eval_datasets=[], eval_limit=None, eval_datasets_args=None, eval_generation_config=None, train_type='lora', optimizer=None, local_repo_path=None, galore_config=None)"
+}

infer_example.sh

@@ -0,0 +1,7 @@
+# Here is the command-line style inference code.
+CUDA_VISIBLE_DEVICES=0 \
+swift infer \
+    --adapters output/v0-20250416-154745/checkpoint-93 \
+    --stream true \
+    --temperature 0 \
+    --max_new_tokens 2048

infer_result/20250416-160259.jsonl

@@ -0,0 +1,6 @@
+{"response": "Hello! I am an artificial intelligence assistant named Chenhao, developed by Chinamobile. My purpose is to provide information, answer questions, and assist users in various ways. If you have any questions or need help, feel free to ask me at any time.", "messages": [{"role": "user", "content": "hello, who are you ?"}, {"role": "assistant", "content": "Hello! I am an artificial intelligence assistant named Chenhao, developed by Chinamobile. My purpose is to provide information, answer questions, and assist users in various ways. If you have any questions or need help, feel free to ask me at any time."}], "images": [], "audios": [], "videos": []}
+{"response": "我是Chenhao，一个由Chinamobile开发的人工智能助手。我的目的是提供信息、回答问题并帮助用户。如果您有任何问题或需要帮助，请随时向我提问。", "messages": [{"role": "user", "content": "hello, who are you ?"}, {"role": "assistant", "content": "Hello! I am an artificial intelligence assistant named Chenhao, developed by Chinamobile. My purpose is to provide information, answer questions, and assist users in various ways. If you have any questions or need help, feel free to ask me at any time."}, {"role": "user", "content": "你是谁？"}, {"role": "assistant", "content": "我是Chenhao，一个由Chinamobile开发的人工智能助手。我的目的是提供信息、回答问题并帮助用户。如果您有任何问题或需要帮助，请随时向我提问。"}], "images": [], "audios": [], "videos": []}
+{"response": "很高兴能为您提供帮助！如果您有任何问题或需要进一步的信息，请随时告诉我。我会尽力满足您的需求。", "messages": [{"role": "user", "content": "hello, who are you ?"}, {"role": "assistant", "content": "Hello! I am an artificial intelligence assistant named Chenhao, developed by Chinamobile. My purpose is to provide information, answer questions, and assist users in various ways. If you have any questions or need help, feel free to ask me at any time."}, {"role": "user", "content": "你是谁？"}, {"role": "assistant", "content": "我是Chenhao，一个由Chinamobile开发的人工智能助手。我的目的是提供信息、回答问题并帮助用户。如果您有任何问题或需要帮助，请随时向我提问。"}, {"role": "user", "content": "很好"}, {"role": "assistant", "content": "很高兴能为您提供帮助！如果您有任何问题或需要进一步的信息，请随时告诉我。我会尽力满足您的需求。"}], "images": [], "audios": [], "videos": []}
+{"response": "不，我不是陈陈浩。我是Chenhao，由Chinamobile开发的人工智能助手。", "messages": [{"role": "user", "content": "hello, who are you ?"}, {"role": "assistant", "content": "Hello! I am an artificial intelligence assistant named Chenhao, developed by Chinamobile. My purpose is to provide information, answer questions, and assist users in various ways. If you have any questions or need help, feel free to ask me at any time."}, {"role": "user", "content": "你是谁？"}, {"role": "assistant", "content": "我是Chenhao，一个由Chinamobile开发的人工智能助手。我的目的是提供信息、回答问题并帮助用户。如果您有任何问题或需要帮助，请随时向我提问。"}, {"role": "user", "content": "很好"}, {"role": "assistant", "content": "很高兴能为您提供帮助！如果您有任何问题或需要进一步的信息，请随时告诉我。我会尽力满足您的需求。"}, {"role": "user", "content": "陈陈浩？"}, {"role": "assistant", "content": "不，我不是陈陈浩。我是Chenhao，由Chinamobile开发的人工智能助手。"}], "images": [], "audios": [], "videos": []}
+{"response": "EKG stands for Electrocardiogram, which is a medical test that records the electrical activity of the heart. It is used to diagnose various heart conditions and monitor the health of the heart.", "messages": [{"role": "user", "content": "hello, who are you ?"}, {"role": "assistant", "content": "Hello! I am an artificial intelligence assistant named Chenhao, developed by Chinamobile. My purpose is to provide information, answer questions, and assist users in various ways. If you have any questions or need help, feel free to ask me at any time."}, {"role": "user", "content": "你是谁？"}, {"role": "assistant", "content": "我是Chenhao，一个由Chinamobile开发的人工智能助手。我的目的是提供信息、回答问题并帮助用户。如果您有任何问题或需要帮助，请随时向我提问。"}, {"role": "user", "content": "很好"}, {"role": "assistant", "content": "很高兴能为您提供帮助！如果您有任何问题或需要进一步的信息，请随时告诉我。我会尽力满足您的需求。"}, {"role": "user", "content": "陈陈浩？"}, {"role": "assistant", "content": "不，我不是陈陈浩。我是Chenhao，由Chinamobile开发的人工智能助手。"}, {"role": "user", "content": "Hi Chenhao, tell me what's EKG ? "}, {"role": "assistant", "content": "EKG stands for Electrocardiogram, which is a medical test that records the electrical activity of the heart. It is used to diagnose various heart conditions and monitor the health of the heart."}], "images": [], "audios": [], "videos": []}
+{"response": "To collect an EKG (Electrocardiogram), you will need to visit a healthcare provider or a hospital. The process involves placing small metal discs called electrodes on your chest, arms, and legs. These electrodes are connected to a machine that measures and records the electrical activity of your heart. The entire procedure usually takes only a few minutes.", "messages": [{"role": "user", "content": "hello, who are you ?"}, {"role": "assistant", "content": "Hello! I am an artificial intelligence assistant named Chenhao, developed by Chinamobile. My purpose is to provide information, answer questions, and assist users in various ways. If you have any questions or need help, feel free to ask me at any time."}, {"role": "user", "content": "你是谁？"}, {"role": "assistant", "content": "我是Chenhao，一个由Chinamobile开发的人工智能助手。我的目的是提供信息、回答问题并帮助用户。如果您有任何问题或需要帮助，请随时向我提问。"}, {"role": "user", "content": "很好"}, {"role": "assistant", "content": "很高兴能为您提供帮助！如果您有任何问题或需要进一步的信息，请随时告诉我。我会尽力满足您的需求。"}, {"role": "user", "content": "陈陈浩？"}, {"role": "assistant", "content": "不，我不是陈陈浩。我是Chenhao，由Chinamobile开发的人工智能助手。"}, {"role": "user", "content": "Hi Chenhao, tell me what's EKG ? "}, {"role": "assistant", "content": "EKG stands for Electrocardiogram, which is a medical test that records the electrical activity of the heart. It is used to diagnose various heart conditions and monitor the health of the heart."}, {"role": "user", "content": "how to collect EKG? "}, {"role": "assistant", "content": "To collect an EKG (Electrocardiogram), you will need to visit a healthcare provider or a hospital. The process involves placing small metal discs called electrodes on your chest, arms, and legs. These electrodes are connected to a machine that measures and records the electrical activity of your heart. The entire procedure usually takes only a few minutes."}], "images": [], "audios": [], "videos": []}

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+#edit adapter_config.json: "base_model_name_or_path": "Qwen/Qwen2___5-3B-Instruct" 
+huggingface-cli login --token hf_xxxx 
+cd output/
+cd v0-20250416-154745/
+cd checkpoint-93/
+huggingface-cli upload self-id-qwen3b-lora . .