Instructions to use second-state/Qwen2.5-Coder-7B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use second-state/Qwen2.5-Coder-7B-Instruct-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="second-state/Qwen2.5-Coder-7B-Instruct-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("second-state/Qwen2.5-Coder-7B-Instruct-GGUF")
model = AutoModelForCausalLM.from_pretrained("second-state/Qwen2.5-Coder-7B-Instruct-GGUF")

llama-cpp-python

How to use second-state/Qwen2.5-Coder-7B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="second-state/Qwen2.5-Coder-7B-Instruct-GGUF",
	filename="Qwen2.5-Coder-7B-Instruct-Q2_K.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use second-state/Qwen2.5-Coder-7B-Instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf second-state/Qwen2.5-Coder-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf second-state/Qwen2.5-Coder-7B-Instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf second-state/Qwen2.5-Coder-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf second-state/Qwen2.5-Coder-7B-Instruct-GGUF:Q4_K_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 second-state/Qwen2.5-Coder-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf second-state/Qwen2.5-Coder-7B-Instruct-GGUF:Q4_K_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 second-state/Qwen2.5-Coder-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf second-state/Qwen2.5-Coder-7B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/second-state/Qwen2.5-Coder-7B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use second-state/Qwen2.5-Coder-7B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "second-state/Qwen2.5-Coder-7B-Instruct-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": "second-state/Qwen2.5-Coder-7B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/second-state/Qwen2.5-Coder-7B-Instruct-GGUF:Q4_K_M

SGLang

How to use second-state/Qwen2.5-Coder-7B-Instruct-GGUF 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 "second-state/Qwen2.5-Coder-7B-Instruct-GGUF" \
    --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": "second-state/Qwen2.5-Coder-7B-Instruct-GGUF",
		"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 "second-state/Qwen2.5-Coder-7B-Instruct-GGUF" \
        --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": "second-state/Qwen2.5-Coder-7B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use second-state/Qwen2.5-Coder-7B-Instruct-GGUF with Ollama:
```
ollama run hf.co/second-state/Qwen2.5-Coder-7B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio new

How to use second-state/Qwen2.5-Coder-7B-Instruct-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 second-state/Qwen2.5-Coder-7B-Instruct-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 second-state/Qwen2.5-Coder-7B-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for second-state/Qwen2.5-Coder-7B-Instruct-GGUF to start chatting

Pi new

How to use second-state/Qwen2.5-Coder-7B-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf second-state/Qwen2.5-Coder-7B-Instruct-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "second-state/Qwen2.5-Coder-7B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use second-state/Qwen2.5-Coder-7B-Instruct-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf second-state/Qwen2.5-Coder-7B-Instruct-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default second-state/Qwen2.5-Coder-7B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use second-state/Qwen2.5-Coder-7B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/second-state/Qwen2.5-Coder-7B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use second-state/Qwen2.5-Coder-7B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull second-state/Qwen2.5-Coder-7B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2.5-Coder-7B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

apepkuss79 commited on Sep 20, 2024

Commit

bbbae08

verified ·

1 Parent(s): ee41dea

Update models

Browse files

Files changed (15) hide show

.gitattributes +13 -0
Qwen2.5-Coder-7B-Instruct-Q2_K.gguf +3 -0
Qwen2.5-Coder-7B-Instruct-Q3_K_L.gguf +3 -0
Qwen2.5-Coder-7B-Instruct-Q3_K_M.gguf +3 -0
Qwen2.5-Coder-7B-Instruct-Q3_K_S.gguf +3 -0
Qwen2.5-Coder-7B-Instruct-Q4_0.gguf +3 -0
Qwen2.5-Coder-7B-Instruct-Q4_K_M.gguf +3 -0
Qwen2.5-Coder-7B-Instruct-Q4_K_S.gguf +3 -0
Qwen2.5-Coder-7B-Instruct-Q5_0.gguf +3 -0
Qwen2.5-Coder-7B-Instruct-Q5_K_M.gguf +3 -0
Qwen2.5-Coder-7B-Instruct-Q5_K_S.gguf +3 -0
Qwen2.5-Coder-7B-Instruct-Q6_K.gguf +3 -0
Qwen2.5-Coder-7B-Instruct-Q8_0.gguf +3 -0
Qwen2.5-Coder-7B-Instruct-f16.gguf +3 -0
config.json +27 -0

@@ -33,3 +33,16 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text

 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Qwen2.5-Coder-7B-Instruct-Q2_K.gguf filter=lfs diff=lfs merge=lfs -text
+Qwen2.5-Coder-7B-Instruct-Q3_K_L.gguf filter=lfs diff=lfs merge=lfs -text
+Qwen2.5-Coder-7B-Instruct-Q3_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+Qwen2.5-Coder-7B-Instruct-Q3_K_S.gguf filter=lfs diff=lfs merge=lfs -text
+Qwen2.5-Coder-7B-Instruct-Q4_0.gguf filter=lfs diff=lfs merge=lfs -text
+Qwen2.5-Coder-7B-Instruct-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+Qwen2.5-Coder-7B-Instruct-Q4_K_S.gguf filter=lfs diff=lfs merge=lfs -text
+Qwen2.5-Coder-7B-Instruct-Q5_0.gguf filter=lfs diff=lfs merge=lfs -text
+Qwen2.5-Coder-7B-Instruct-Q5_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+Qwen2.5-Coder-7B-Instruct-Q5_K_S.gguf filter=lfs diff=lfs merge=lfs -text
+Qwen2.5-Coder-7B-Instruct-Q6_K.gguf filter=lfs diff=lfs merge=lfs -text
+Qwen2.5-Coder-7B-Instruct-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
+Qwen2.5-Coder-7B-Instruct-f16.gguf filter=lfs diff=lfs merge=lfs -text

+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "hidden_act": "silu",
+  "hidden_size": 3584,
+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 1000000.0,
+  "sliding_window": 131072,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.44.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

+version https://git-lfs.github.com/spec/v1
+oid sha256:bbb11377c336dc69d6bb576db4088f310da141c78045133cca9f434b37cf5c52
+size 3015940608

+version https://git-lfs.github.com/spec/v1
+oid sha256:843a07deb68947c60261195deec4ce7ccd3cc2c6ea8b26e3ae6889712ec942af
+size 4088459776

+version https://git-lfs.github.com/spec/v1
+oid sha256:7af9722d4eba7d108e7ee611f95049ec5e223481a2537f165d4f401e453033b1
+size 3808391680

+version https://git-lfs.github.com/spec/v1
+oid sha256:a9009b5b3583068b6bf1e2846c08dc37c0a01597a16f92467ca1487e57cd8a34
+size 3492368896

+version https://git-lfs.github.com/spec/v1
+oid sha256:3a49244119e8604dbdb874e8c1bf9ccbc967e3eafc5b58527407d9b4a94ed99b
+size 4431391232

+version https://git-lfs.github.com/spec/v1
+oid sha256:7ec3d5cfd560c5eac34e6c377c0bbc2bda389b282dca2b28bc31e621d26929a7
+size 4683074048

+version https://git-lfs.github.com/spec/v1
+oid sha256:d280846d836ce2cdbe1520f5f6d90818aa4f67c88180282be565c46eb2f4e715
+size 4457769472

+version https://git-lfs.github.com/spec/v1
+oid sha256:19686bbe6c62340ded5a576e4bdf3474a7da2750749210561b546cd616d83cca
+size 5315176960

+version https://git-lfs.github.com/spec/v1
+oid sha256:84d37734ac254859ae51aa94d2d7d80ef131a2036f461b19c1d8aa22ff07bac8
+size 5444831744

+version https://git-lfs.github.com/spec/v1
+oid sha256:ec5e1b9505d6b62703ea83578347e0787a653a13c4eb01d025957ccbe06a09d3
+size 6254199296

	@@ -0,0 +1,3 @@

	@@ -0,0 +1,3 @@

	@@ -0,0 +1,3 @@

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:24b532e5276503b147d0eea0e47cb1d2bcce7c9034edd657b624261862ca54a1
+size 8098525696

+version https://git-lfs.github.com/spec/v1
+oid sha256:d8e374f171cbf20a79619dc6e0926124c7690497609bc94bdad18ef6a86cdee0
+size 15237853696