Instructions to use WisdomShell/CodeShell-7B-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WisdomShell/CodeShell-7B-Chat with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="WisdomShell/CodeShell-7B-Chat", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("WisdomShell/CodeShell-7B-Chat", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use WisdomShell/CodeShell-7B-Chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WisdomShell/CodeShell-7B-Chat"
# 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-Chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/WisdomShell/CodeShell-7B-Chat

SGLang

How to use WisdomShell/CodeShell-7B-Chat 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-Chat" \
    --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-Chat",
		"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-Chat" \
        --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-Chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use WisdomShell/CodeShell-7B-Chat with Docker Model Runner:
```
docker model run hf.co/WisdomShell/CodeShell-7B-Chat
```

ruixie commited on Oct 20, 2023

Commit

aa4e28f

1 Parent(s): c5390f9

Update modeling_codeshell.py

Browse files

Files changed (1) hide show

modeling_codeshell.py +2 -2

modeling_codeshell.py CHANGED Viewed

@@ -970,7 +970,7 @@ class CodeShellForCausalLM(CodeShellPreTrainedModel):
         generation_config = generation_config or self.generation_config
         input_ids = self.build_chat_input(query, history, tokenizer, generation_config.max_new_tokens)
         stopping_criteria = StoppingCriteriaList(
-            [EndOfFunctionCriteria([len(input_ids[0])], ['|end|', '|<end>|', '<|endoftext|>'], tokenizer)]
         )
         if stream:
@@ -994,7 +994,7 @@ class CodeShellForCausalLM(CodeShellPreTrainedModel):
         input_ids = input_ids[-max_input_tokens:]  # truncate left
         stopping_criteria = StoppingCriteriaList(
-            [EndOfFunctionCriteria([len(input_ids[0])], ['|end|', '|<end>|', '<|endoftext|>'], tokenizer)]
         )
         streamer = TextIterStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

         generation_config = generation_config or self.generation_config
         input_ids = self.build_chat_input(query, history, tokenizer, generation_config.max_new_tokens)
         stopping_criteria = StoppingCriteriaList(
+            [EndOfFunctionCriteria([len(input_ids[0])], ['|<end>|', '|end|', '<|endoftext|>', '## human'], tokenizer)]
         )
         if stream:
         input_ids = input_ids[-max_input_tokens:]  # truncate left
         stopping_criteria = StoppingCriteriaList(
+            [EndOfFunctionCriteria([len(input_ids[0])], ['|<end>|', '|end|', '<|endoftext|>', '## human'], tokenizer)]
         )
         streamer = TextIterStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)