Text Generation
Transformers
Safetensors
Chinese
qwen2
medical
conversational
text-generation-inference
Instructions to use jun10k/Qwen1.5-7B-MeChat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jun10k/Qwen1.5-7B-MeChat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jun10k/Qwen1.5-7B-MeChat") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("jun10k/Qwen1.5-7B-MeChat") model = AutoModelForCausalLM.from_pretrained("jun10k/Qwen1.5-7B-MeChat") 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 jun10k/Qwen1.5-7B-MeChat with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jun10k/Qwen1.5-7B-MeChat" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jun10k/Qwen1.5-7B-MeChat", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/jun10k/Qwen1.5-7B-MeChat
- SGLang
How to use jun10k/Qwen1.5-7B-MeChat 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 "jun10k/Qwen1.5-7B-MeChat" \ --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": "jun10k/Qwen1.5-7B-MeChat", "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 "jun10k/Qwen1.5-7B-MeChat" \ --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": "jun10k/Qwen1.5-7B-MeChat", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use jun10k/Qwen1.5-7B-MeChat with Docker Model Runner:
docker model run hf.co/jun10k/Qwen1.5-7B-MeChat
Upload folder using huggingface_hub
Browse files- added_tokens.json +5 -0
- config.json +28 -0
- configuration.json +5 -0
- generation_config.json +10 -0
- infer_result_20240426-014040.jsonl +1 -0
- infer_result_20240426-014154.jsonl +2 -0
- merges.txt +0 -0
- model-00001-of-00006.safetensors +3 -0
- model-00002-of-00006.safetensors +3 -0
- model-00003-of-00006.safetensors +3 -0
- model-00004-of-00006.safetensors +3 -0
- model-00005-of-00006.safetensors +3 -0
- model-00006-of-00006.safetensors +3 -0
- model.safetensors.index.json +394 -0
- sft_args.json +170 -0
- special_tokens_map.json +20 -0
- tokenizer.json +0 -0
- tokenizer_config.json +43 -0
- vocab.json +0 -0
added_tokens.json
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{
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"<|endoftext|>": 151643,
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"<|im_end|>": 151645,
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"<|im_start|>": 151644
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}
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config.json
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{
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"_name_or_path": "/home/cladmin/.cache/modelscope/hub/qwen/Qwen1___5-7B",
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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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"bos_token_id": 151643,
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"eos_token_id": 151643,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 11008,
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"max_position_embeddings": 32768,
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"max_window_layers": 28,
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"model_type": "qwen2",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 32,
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"rms_norm_eps": 1e-06,
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"rope_theta": 1000000.0,
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"sliding_window": 32768,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.39.3",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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configuration.json
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{
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"framework": "pytorch",
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"task": "fill-mask",
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"allow_remote": true
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}
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generation_config.json
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{
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"do_sample": true,
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"eos_token_id": 151643,
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"max_new_tokens": 2048,
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"pad_token_id": 151643,
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"temperature": 0.3,
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"top_k": 20,
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"top_p": 0.7,
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"transformers_version": "4.39.3"
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}
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infer_result_20240426-014040.jsonl
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{"query": "我最近高考压力很大", "response": "高考确实是一个重要的考试,会带来很大的压力。你可以试试和身边的人分享你的感受,或者找一些放松的方式来缓解压力,比如运动、听音乐或者和朋友聊天。", "history": []}
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infer_result_20240426-014154.jsonl
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{"query": "我最近准备高考,感觉压力很大", "response": "高考确实是一个重要的考试,会给你带来很大的压力。但是你要相信自己的能力,相信你已经做了充分的准备。同时,也要学会放松自己,找到适合自己的减压方式,比如运动、听音乐、与朋友聊天等。记住,你不是一个人在战斗,身边还有很多人支持你。", "history": []}
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{"query": "我现在完全没有学习的动力和方向,我对高考一点信心都没有了", "response": "我理解你现在的困惑和失望。但是请相信,高考只是人生中的一小部分,它并不代表你的全部价值和未来。你可以尝试寻找一些激励自己的方法,比如与身边的朋友交流,听听他们的故事和经验,或者寻求一些专业的心理咨询师的帮助。同时,也要学会给自己一些积极的肯定和鼓励,相信自己的能力,相信自己能够克服困难。", "history": [["我最近准备高考,感觉压力很大", "高考确实是一个重要的考试,会给你带来很大的压力。但是你要相信自己的能力,相信你已经做了充分的准备。同时,也要学会放松自己,找到适合自己的减压方式,比如运动、听音乐、与朋友聊天等。记住,你不是一个人在战斗,身边还有很多人支持你。"]]}
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merges.txt
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The diff for this file is too large to render.
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model-00001-of-00006.safetensors
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version https://git-lfs.github.com/spec/v1
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model-00002-of-00006.safetensors
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version https://git-lfs.github.com/spec/v1
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model-00003-of-00006.safetensors
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version https://git-lfs.github.com/spec/v1
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model-00004-of-00006.safetensors
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model-00005-of-00006.safetensors
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model-00006-of-00006.safetensors
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version https://git-lfs.github.com/spec/v1
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model.safetensors.index.json
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sft_args.json
ADDED
|
@@ -0,0 +1,170 @@
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| 1 |
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| 169 |
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"training_args": "Seq2SeqTrainingArguments(output_dir='/home/cladmin/swift/output/qwen1half-7b/v0-20240422-220038', overwrite_output_dir=False, do_train=False, do_eval=True, do_predict=False, evaluation_strategy=<IntervalStrategy.STEPS: 'steps'>, prediction_loss_only=False, per_device_train_batch_size=5, per_device_eval_batch_size=5, per_gpu_train_batch_size=None, per_gpu_eval_batch_size=None, gradient_accumulation_steps=4, eval_accumulation_steps=None, eval_delay=0, learning_rate=0.0001, weight_decay=0.1, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, max_grad_norm=0.5, num_train_epochs=2, max_steps=-1, lr_scheduler_type=<SchedulerType.LINEAR: 'linear'>, lr_scheduler_kwargs={}, warmup_ratio=0.01, warmup_steps=0, log_level='passive', log_level_replica='warning', log_on_each_node=True, logging_dir='/home/cladmin/swift/output/qwen1half-7b/v0-20240422-220038/runs', logging_strategy=<IntervalStrategy.STEPS: 'steps'>, logging_first_step=True, logging_steps=5, logging_nan_inf_filter=True, save_strategy=<IntervalStrategy.STEPS: 'steps'>, save_steps=10000, save_total_limit=2, save_safetensors=True, save_on_each_node=True, save_only_model=False, no_cuda=False, use_cpu=False, use_mps_device=False, seed=42, data_seed=None, 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=2000, dataloader_num_workers=1, dataloader_prefetch_factor=None, past_index=-1, run_name='/home/cladmin/swift/output/qwen1half-7b/v0-20240422-220038', 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}, fsdp_transformer_layer_cls_to_wrap=None, accelerator_config=AcceleratorConfig(split_batches=False, dispatch_batches=None, even_batches=True, use_seedable_sampler=True), 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=['wandb'], 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=False, hub_always_push=False, gradient_checkpointing=True, gradient_checkpointing_kwargs=None, include_inputs_for_metrics=False, 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, dispatch_batches=None, split_batches=None, include_tokens_per_second=False, include_num_input_tokens_seen=False, neftune_noise_alpha=None, optim_target_modules=None, sortish_sampler=True, predict_with_generate=False, generation_max_length=None, generation_num_beams=None, generation_config=GenerationConfig {\n \"do_sample\": true,\n \"eos_token_id\": 151643,\n \"max_new_tokens\": 2048,\n \"pad_token_id\": 151643,\n \"temperature\": 0.3,\n \"top_k\": 20,\n \"top_p\": 0.7\n}\n, train_sampler_random=True, push_hub_strategy='push_best', acc_strategy='token', additional_saved_files=[])"
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}
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special_tokens_map.json
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{
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| 2 |
+
"additional_special_tokens": [
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| 3 |
+
"<|im_start|>",
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| 4 |
+
"<|im_end|>"
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| 5 |
+
],
|
| 6 |
+
"eos_token": {
|
| 7 |
+
"content": "<|endoftext|>",
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| 8 |
+
"lstrip": false,
|
| 9 |
+
"normalized": false,
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| 10 |
+
"rstrip": false,
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| 11 |
+
"single_word": false
|
| 12 |
+
},
|
| 13 |
+
"pad_token": {
|
| 14 |
+
"content": "<|endoftext|>",
|
| 15 |
+
"lstrip": false,
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| 16 |
+
"normalized": false,
|
| 17 |
+
"rstrip": false,
|
| 18 |
+
"single_word": false
|
| 19 |
+
}
|
| 20 |
+
}
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tokenizer.json
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tokenizer_config.json
ADDED
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| 1 |
+
{
|
| 2 |
+
"add_prefix_space": false,
|
| 3 |
+
"added_tokens_decoder": {
|
| 4 |
+
"151643": {
|
| 5 |
+
"content": "<|endoftext|>",
|
| 6 |
+
"lstrip": false,
|
| 7 |
+
"normalized": false,
|
| 8 |
+
"rstrip": false,
|
| 9 |
+
"single_word": false,
|
| 10 |
+
"special": true
|
| 11 |
+
},
|
| 12 |
+
"151644": {
|
| 13 |
+
"content": "<|im_start|>",
|
| 14 |
+
"lstrip": false,
|
| 15 |
+
"normalized": false,
|
| 16 |
+
"rstrip": false,
|
| 17 |
+
"single_word": false,
|
| 18 |
+
"special": true
|
| 19 |
+
},
|
| 20 |
+
"151645": {
|
| 21 |
+
"content": "<|im_end|>",
|
| 22 |
+
"lstrip": false,
|
| 23 |
+
"normalized": false,
|
| 24 |
+
"rstrip": false,
|
| 25 |
+
"single_word": false,
|
| 26 |
+
"special": true
|
| 27 |
+
}
|
| 28 |
+
},
|
| 29 |
+
"additional_special_tokens": [
|
| 30 |
+
"<|im_start|>",
|
| 31 |
+
"<|im_end|>"
|
| 32 |
+
],
|
| 33 |
+
"bos_token": null,
|
| 34 |
+
"chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
|
| 35 |
+
"clean_up_tokenization_spaces": false,
|
| 36 |
+
"eos_token": "<|endoftext|>",
|
| 37 |
+
"errors": "replace",
|
| 38 |
+
"model_max_length": 32768,
|
| 39 |
+
"pad_token": "<|endoftext|>",
|
| 40 |
+
"split_special_tokens": false,
|
| 41 |
+
"tokenizer_class": "Qwen2Tokenizer",
|
| 42 |
+
"unk_token": null
|
| 43 |
+
}
|
vocab.json
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