Text Generation
Transformers
PyTorch
Safetensors
Chinese
English
kclgpt
codeshell
wisdomshell
pku-kcl
openbankai
custom_code
Instructions to use WisdomShell/CodeShell-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WisdomShell/CodeShell-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="WisdomShell/CodeShell-7B", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("WisdomShell/CodeShell-7B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use WisdomShell/CodeShell-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "WisdomShell/CodeShell-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "WisdomShell/CodeShell-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/WisdomShell/CodeShell-7B
- SGLang
How to use WisdomShell/CodeShell-7B 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 "WisdomShell/CodeShell-7B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "WisdomShell/CodeShell-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "WisdomShell/CodeShell-7B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "WisdomShell/CodeShell-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use WisdomShell/CodeShell-7B with Docker Model Runner:
docker model run hf.co/WisdomShell/CodeShell-7B
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"_name_or_path": "WisdomShell/CodeShell",
"activation_function": "gelu_pytorch_tanh",
"architectures": [
"CodeShellForCausalLM"
],
"attention_softmax_in_fp32": true,
"attn_pdrop": 0.1,
"auto_map": {
"AutoConfig": "configuration_codeshell.CodeShellConfig",
"AutoModelForCausalLM": "modeling_codeshell.CodeShellForCausalLM"
},
"group_query_attention": true,
"num_query_groups": 8,
"position_embedding_type": "rope",
"bos_token_id": 70000,
"eos_token_id": 70000,
"vocab_size": 70144,
"embd_pdrop": 0.1,
"inference_runner": 0,
"initializer_range": 0.02,
"layer_norm_epsilon": 1e-05,
"max_batch_size": null,
"max_sequence_length": null,
"model_type": "kclgpt",
"n_layer": 42,
"n_embd": 4096,
"n_inner": 16384,
"n_head": 32,
"n_positions": 8192,
"pad_key_length": true,
"resid_pdrop": 0.1,
"rope_scaling": null,
"pre_allocate_kv_cache": false,
"scale_attention_softmax_in_fp32": true,
"scale_attn_weights": true,
"summary_activation": null,
"summary_first_dropout": 0.1,
"summary_proj_to_labels": true,
"summary_type": "cls_index",
"summary_use_proj": true,
"torch_dtype": "bfloat16",
"transformers_version": "4.31.0",
"use_cache": true,
"validate_runner_input": true
}
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