Instructions to use WisdomShell/CodeShell-7B-Chat-int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WisdomShell/CodeShell-7B-Chat-int4 with Transformers:

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

pipe = pipeline("text-generation", model="WisdomShell/CodeShell-7B-Chat-int4", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("WisdomShell/CodeShell-7B-Chat-int4", trust_remote_code=True, dtype="auto")

llama-cpp-python

How to use WisdomShell/CodeShell-7B-Chat-int4 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="WisdomShell/CodeShell-7B-Chat-int4",
	filename="codeshell-chat-q4_0.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use WisdomShell/CodeShell-7B-Chat-int4 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf WisdomShell/CodeShell-7B-Chat-int4:Q4_0
# Run inference directly in the terminal:
llama-cli -hf WisdomShell/CodeShell-7B-Chat-int4:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf WisdomShell/CodeShell-7B-Chat-int4:Q4_0
# Run inference directly in the terminal:
llama-cli -hf WisdomShell/CodeShell-7B-Chat-int4:Q4_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf WisdomShell/CodeShell-7B-Chat-int4:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf WisdomShell/CodeShell-7B-Chat-int4:Q4_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf WisdomShell/CodeShell-7B-Chat-int4:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf WisdomShell/CodeShell-7B-Chat-int4:Q4_0

Use Docker

docker model run hf.co/WisdomShell/CodeShell-7B-Chat-int4:Q4_0

LM Studio
Jan

vLLM

How to use WisdomShell/CodeShell-7B-Chat-int4 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-int4"
# 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-int4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/WisdomShell/CodeShell-7B-Chat-int4:Q4_0

SGLang

How to use WisdomShell/CodeShell-7B-Chat-int4 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-int4" \
    --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-int4",
		"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-int4" \
        --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-int4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use WisdomShell/CodeShell-7B-Chat-int4 with Ollama:
```
ollama run hf.co/WisdomShell/CodeShell-7B-Chat-int4:Q4_0
```

Unsloth Studio new

How to use WisdomShell/CodeShell-7B-Chat-int4 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for WisdomShell/CodeShell-7B-Chat-int4 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for WisdomShell/CodeShell-7B-Chat-int4 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for WisdomShell/CodeShell-7B-Chat-int4 to start chatting

Docker Model Runner
How to use WisdomShell/CodeShell-7B-Chat-int4 with Docker Model Runner:
```
docker model run hf.co/WisdomShell/CodeShell-7B-Chat-int4:Q4_0
```

Lemonade

How to use WisdomShell/CodeShell-7B-Chat-int4 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull WisdomShell/CodeShell-7B-Chat-int4:Q4_0

Run and chat with the model

lemonade run user.CodeShell-7B-Chat-int4-Q4_0

List all available models

lemonade list

ruixie commited on Oct 20, 2023

Commit

38b8ac6

1 Parent(s): 35daae6

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)