Instructions to use CISCai/CodeQwen1.5-7B-Chat-SOTA-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CISCai/CodeQwen1.5-7B-Chat-SOTA-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="CISCai/CodeQwen1.5-7B-Chat-SOTA-GGUF",
	filename="CodeQwen1.5-7B-Chat.IQ1_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use CISCai/CodeQwen1.5-7B-Chat-SOTA-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf CISCai/CodeQwen1.5-7B-Chat-SOTA-GGUF:IQ1_M
# Run inference directly in the terminal:
llama cli -hf CISCai/CodeQwen1.5-7B-Chat-SOTA-GGUF:IQ1_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf CISCai/CodeQwen1.5-7B-Chat-SOTA-GGUF:IQ1_M
# Run inference directly in the terminal:
llama cli -hf CISCai/CodeQwen1.5-7B-Chat-SOTA-GGUF:IQ1_M

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 CISCai/CodeQwen1.5-7B-Chat-SOTA-GGUF:IQ1_M
# Run inference directly in the terminal:
./llama-cli -hf CISCai/CodeQwen1.5-7B-Chat-SOTA-GGUF:IQ1_M

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 CISCai/CodeQwen1.5-7B-Chat-SOTA-GGUF:IQ1_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf CISCai/CodeQwen1.5-7B-Chat-SOTA-GGUF:IQ1_M

Use Docker

docker model run hf.co/CISCai/CodeQwen1.5-7B-Chat-SOTA-GGUF:IQ1_M

LM Studio
Jan

vLLM

How to use CISCai/CodeQwen1.5-7B-Chat-SOTA-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CISCai/CodeQwen1.5-7B-Chat-SOTA-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CISCai/CodeQwen1.5-7B-Chat-SOTA-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CISCai/CodeQwen1.5-7B-Chat-SOTA-GGUF:IQ1_M

Ollama
How to use CISCai/CodeQwen1.5-7B-Chat-SOTA-GGUF with Ollama:
```
ollama run hf.co/CISCai/CodeQwen1.5-7B-Chat-SOTA-GGUF:IQ1_M
```

Unsloth Studio

How to use CISCai/CodeQwen1.5-7B-Chat-SOTA-GGUF 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 CISCai/CodeQwen1.5-7B-Chat-SOTA-GGUF 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 CISCai/CodeQwen1.5-7B-Chat-SOTA-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for CISCai/CodeQwen1.5-7B-Chat-SOTA-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use CISCai/CodeQwen1.5-7B-Chat-SOTA-GGUF with Docker Model Runner:
```
docker model run hf.co/CISCai/CodeQwen1.5-7B-Chat-SOTA-GGUF:IQ1_M
```

Lemonade

How to use CISCai/CodeQwen1.5-7B-Chat-SOTA-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull CISCai/CodeQwen1.5-7B-Chat-SOTA-GGUF:IQ1_M

Run and chat with the model

lemonade run user.CodeQwen1.5-7B-Chat-SOTA-GGUF-IQ1_M

List all available models

lemonade list

CISCai commited on Apr 24, 2024

Commit

1569a0c

verified ·

1 Parent(s): c5a8f50

add dataset

Browse files

Files changed (1) hide show

README.md +3 -1

README.md CHANGED Viewed

@@ -12,6 +12,8 @@ tags:
 model_creator: Qwen
 model_name: CodeQwen1.5-7B-Chat
 model_type: qwen2
 quantized_by: CISC
 ---
@@ -105,7 +107,7 @@ Generated importance matrix file: [CodeQwen1.5-7B-Chat.imatrix.dat](https://hugg
 Make sure you are using `llama.cpp` from commit [0becb22](https://github.com/ggerganov/llama.cpp/commit/0becb22ac05b6542bd9d5f2235691aa1d3d4d307) or later.
 ```shell
-./main -ngl 33 -m CodeQwen1.5-7B-Chat.IQ2_XS.gguf --color -c 65536 --temp 1.0 --repeat-penalty 1.0 --top-p 0.95 -n -1 -p ""<|im_start|>system\nYou are a helpful assistant<|im_end|>\n<|im_start|>\n{prompt}<|im_end|>\n<|im_start|>assistant\n"
 ```
 Change `-ngl 33` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.

 model_creator: Qwen
 model_name: CodeQwen1.5-7B-Chat
 model_type: qwen2
+datasets:
+- m-a-p/CodeFeedback-Filtered-Instruction
 quantized_by: CISC
 ---
 Make sure you are using `llama.cpp` from commit [0becb22](https://github.com/ggerganov/llama.cpp/commit/0becb22ac05b6542bd9d5f2235691aa1d3d4d307) or later.
 ```shell
+./main -ngl 33 -m CodeQwen1.5-7B-Chat.IQ2_XS.gguf --color -c 65536 --temp 1.0 --repeat-penalty 1.0 --top-p 0.95 -n -1 -p "<|im_start|>system\nYou are a helpful assistant<|im_end|>\n<|im_start|>\n{prompt}<|im_end|>\n<|im_start|>assistant\n"
 ```
 Change `-ngl 33` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.