Text Generation
Transformers
Safetensors
English
qwen2
audio
speech
conversational
custom_code
text-generation-inference
Instructions to use ddwang2000/EchoChat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ddwang2000/EchoChat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ddwang2000/EchoChat", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ddwang2000/EchoChat", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("ddwang2000/EchoChat", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ddwang2000/EchoChat with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ddwang2000/EchoChat" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ddwang2000/EchoChat", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ddwang2000/EchoChat
- SGLang
How to use ddwang2000/EchoChat 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 "ddwang2000/EchoChat" \ --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": "ddwang2000/EchoChat", "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 "ddwang2000/EchoChat" \ --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": "ddwang2000/EchoChat", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use ddwang2000/EchoChat with Docker Model Runner:
docker model run hf.co/ddwang2000/EchoChat
Download audio_encoder/config.json from ddwang2000/EchoChat: direct link, hf CLI and curl.
- Browser
- Download file 2.44 kB
-
https://huggingface.co/ddwang2000/EchoChat/resolve/main/audio_encoder/config.json
- Command line
-
hf download hf://ddwang2000/EchoChat/audio_encoder/config.json
-
curl -L -o config.json https://huggingface.co/ddwang2000/EchoChat/resolve/main/audio_encoder/config.json
2.44 kB
| { | |
| "vocab_size": 51866, | |
| "num_mel_bins": 128, | |
| "d_model": 1280, | |
| "encoder_layers": 32, | |
| "encoder_attention_heads": 20, | |
| "decoder_layers": 32, | |
| "decoder_attention_heads": 20, | |
| "decoder_ffn_dim": 5120, | |
| "encoder_ffn_dim": 5120, | |
| "dropout": 0.0, | |
| "attention_dropout": 0.0, | |
| "activation_dropout": 0.0, | |
| "activation_function": "gelu", | |
| "init_std": 0.02, | |
| "encoder_layerdrop": 0.0, | |
| "decoder_layerdrop": 0.0, | |
| "use_cache": true, | |
| "num_hidden_layers": 32, | |
| "scale_embedding": false, | |
| "max_source_positions": 1500, | |
| "max_target_positions": 448, | |
| "classifier_proj_size": 256, | |
| "use_weighted_layer_sum": false, | |
| "apply_spec_augment": false, | |
| "mask_time_prob": 0.05, | |
| "mask_time_length": 10, | |
| "mask_time_min_masks": 2, | |
| "mask_feature_prob": 0.0, | |
| "mask_feature_length": 10, | |
| "mask_feature_min_masks": 0, | |
| "median_filter_width": 7, | |
| "return_dict": true, | |
| "output_hidden_states": false, | |
| "output_attentions": false, | |
| "torchscript": false, | |
| "torch_dtype": "bfloat16", | |
| "use_bfloat16": false, | |
| "tf_legacy_loss": false, | |
| "pruned_heads": {}, | |
| "tie_word_embeddings": true, | |
| "chunk_size_feed_forward": 0, | |
| "is_encoder_decoder": true, | |
| "is_decoder": false, | |
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| "add_cross_attention": false, | |
| "tie_encoder_decoder": false, | |
| "max_length": 448, | |
| "min_length": 0, | |
| "do_sample": false, | |
| "early_stopping": false, | |
| "num_beams": 1, | |
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| "diversity_penalty": 0.0, | |
| "temperature": 1.0, | |
| "top_k": 50, | |
| "top_p": 1.0, | |
| "typical_p": 1.0, | |
| "repetition_penalty": 1.0, | |
| "length_penalty": 1.0, | |
| "no_repeat_ngram_size": 0, | |
| "encoder_no_repeat_ngram_size": 0, | |
| "bad_words_ids": null, | |
| "num_return_sequences": 1, | |
| "output_scores": false, | |
| "return_dict_in_generate": false, | |
| "forced_bos_token_id": null, | |
| "forced_eos_token_id": null, | |
| "remove_invalid_values": false, | |
| "exponential_decay_length_penalty": null, | |
| "suppress_tokens": null, | |
| "begin_suppress_tokens": [ | |
| 220, | |
| 50257 | |
| ], | |
| "architectures": [ | |
| "WhisperEncoder" | |
| ], | |
| "id2label": { | |
| "0": "LABEL_0", | |
| "1": "LABEL_1" | |
| }, | |
| "label2id": { | |
| "LABEL_0": 0, | |
| "LABEL_1": 1 | |
| }, | |
| "tokenizer_class": null, | |
| "prefix": null, | |
| "bos_token_id": 50257, | |
| "pad_token_id": 50256, | |
| "eos_token_id": 50257, | |
| "sep_token_id": null, | |
| "decoder_start_token_id": 50258, | |
| "problem_type": null, | |
| "_attn_implementation_autoset": true, | |
| "transformers_version": "4.51.3", | |
| "model_type": "whisper" | |
| } | |