Text Generation
Transformers
Safetensors
OpenVINO
English
qwen2
code
codeqwen
chat
qwen
qwen-coder
nncf
8-bit precision
conversational
text-generation-inference
Instructions to use Fetching/Qwen2.5-Coder-7B-Instruct-openvino-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Fetching/Qwen2.5-Coder-7B-Instruct-openvino-8bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Fetching/Qwen2.5-Coder-7B-Instruct-openvino-8bit") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Fetching/Qwen2.5-Coder-7B-Instruct-openvino-8bit") model = AutoModelForCausalLM.from_pretrained("Fetching/Qwen2.5-Coder-7B-Instruct-openvino-8bit") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Fetching/Qwen2.5-Coder-7B-Instruct-openvino-8bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Fetching/Qwen2.5-Coder-7B-Instruct-openvino-8bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Fetching/Qwen2.5-Coder-7B-Instruct-openvino-8bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Fetching/Qwen2.5-Coder-7B-Instruct-openvino-8bit
- SGLang
How to use Fetching/Qwen2.5-Coder-7B-Instruct-openvino-8bit 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 "Fetching/Qwen2.5-Coder-7B-Instruct-openvino-8bit" \ --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": "Fetching/Qwen2.5-Coder-7B-Instruct-openvino-8bit", "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 "Fetching/Qwen2.5-Coder-7B-Instruct-openvino-8bit" \ --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": "Fetching/Qwen2.5-Coder-7B-Instruct-openvino-8bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Fetching/Qwen2.5-Coder-7B-Instruct-openvino-8bit with Docker Model Runner:
docker model run hf.co/Fetching/Qwen2.5-Coder-7B-Instruct-openvino-8bit
Upload openvino_config.json with huggingface_hub
Browse files- openvino_config.json +27 -0
openvino_config.json
ADDED
|
@@ -0,0 +1,27 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"dtype": "int8",
|
| 3 |
+
"input_info": null,
|
| 4 |
+
"optimum_version": "1.25.2",
|
| 5 |
+
"quantization_config": {
|
| 6 |
+
"all_layers": null,
|
| 7 |
+
"backup_precision": null,
|
| 8 |
+
"bits": 8,
|
| 9 |
+
"dataset": null,
|
| 10 |
+
"dtype": "int8",
|
| 11 |
+
"gptq": null,
|
| 12 |
+
"group_size": -1,
|
| 13 |
+
"ignored_scope": null,
|
| 14 |
+
"lora_correction": null,
|
| 15 |
+
"num_samples": null,
|
| 16 |
+
"processor": null,
|
| 17 |
+
"quant_method": "default",
|
| 18 |
+
"ratio": 1.0,
|
| 19 |
+
"scale_estimation": null,
|
| 20 |
+
"sensitivity_metric": null,
|
| 21 |
+
"sym": false,
|
| 22 |
+
"tokenizer": null,
|
| 23 |
+
"trust_remote_code": false
|
| 24 |
+
},
|
| 25 |
+
"save_onnx_model": false,
|
| 26 |
+
"transformers_version": "4.46.3"
|
| 27 |
+
}
|