Text Generation
Transformers
Safetensors
English
qwen3
text-generation-inference
unsloth
conversational
Instructions to use lmq1909/Qwen3-8B-LQA-3e-GRPO-100s-save2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lmq1909/Qwen3-8B-LQA-3e-GRPO-100s-save2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="lmq1909/Qwen3-8B-LQA-3e-GRPO-100s-save2") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("lmq1909/Qwen3-8B-LQA-3e-GRPO-100s-save2") model = AutoModelForCausalLM.from_pretrained("lmq1909/Qwen3-8B-LQA-3e-GRPO-100s-save2") 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 Settings
- vLLM
How to use lmq1909/Qwen3-8B-LQA-3e-GRPO-100s-save2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lmq1909/Qwen3-8B-LQA-3e-GRPO-100s-save2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lmq1909/Qwen3-8B-LQA-3e-GRPO-100s-save2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/lmq1909/Qwen3-8B-LQA-3e-GRPO-100s-save2
- SGLang
How to use lmq1909/Qwen3-8B-LQA-3e-GRPO-100s-save2 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 "lmq1909/Qwen3-8B-LQA-3e-GRPO-100s-save2" \ --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": "lmq1909/Qwen3-8B-LQA-3e-GRPO-100s-save2", "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 "lmq1909/Qwen3-8B-LQA-3e-GRPO-100s-save2" \ --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": "lmq1909/Qwen3-8B-LQA-3e-GRPO-100s-save2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio
How to use lmq1909/Qwen3-8B-LQA-3e-GRPO-100s-save2 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 lmq1909/Qwen3-8B-LQA-3e-GRPO-100s-save2 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 lmq1909/Qwen3-8B-LQA-3e-GRPO-100s-save2 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for lmq1909/Qwen3-8B-LQA-3e-GRPO-100s-save2 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="lmq1909/Qwen3-8B-LQA-3e-GRPO-100s-save2", max_seq_length=2048, ) - Docker Model Runner
How to use lmq1909/Qwen3-8B-LQA-3e-GRPO-100s-save2 with Docker Model Runner:
docker model run hf.co/lmq1909/Qwen3-8B-LQA-3e-GRPO-100s-save2
| {%- if not add_generation_prompt is defined -%} | |
| {%- set add_generation_prompt = false -%} | |
| {%- endif -%} | |
| {%- set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt="", is_first_sp=true, is_last_user=false) -%} | |
| {%- for message in messages -%} | |
| {%- if message["role"] == "system" -%} | |
| {%- if ns.is_first_sp -%} | |
| {%- set ns.system_prompt = ns.system_prompt + message["content"] -%} | |
| {%- set ns.is_first_sp = false -%} | |
| {%- else -%} | |
| {%- set ns.system_prompt = ns.system_prompt + "\n\n" + message["content"] -%} | |
| {%- endif -%} | |
| {%- endif -%} | |
| {%- endfor -%} | |
| {{- bos_token -}} | |
| {{- ns.system_prompt -}} | |
| {%- for message in messages -%} | |
| {%- set content = message["content"] -%} | |
| {%- if message["role"] == "user" -%} | |
| {%- set ns.is_tool = false -%} | |
| {%- set ns.is_first = false -%} | |
| {%- set ns.is_last_user = true -%} | |
| {{- "<|User|>" + content + "<|Assistant|>" -}} | |
| {%- endif -%} | |
| {%- if message["role"] == "assistant" and message["tool_calls"] is defined and message["tool_calls"] is not none -%} | |
| {%- set ns.is_last_user = false -%} | |
| {%- if ns.is_tool -%} | |
| {{- "<|tool▁outputs▁end|>" -}} | |
| {%- endif -%} | |
| {%- set ns.is_first = false -%} | |
| {%- set ns.is_tool = false -%} | |
| {%- set ns.is_output_first = true -%} | |
| {%- for tool in message["tool_calls"] -%} | |
| {%- if not ns.is_first -%} | |
| {%- if content is none -%} | |
| {{- "<|tool▁calls▁begin|><|tool▁call▁begin|>" + tool["type"] + "<|tool▁sep|>" + tool["function"]["name"] + "\n```json\n" + tool["function"]["arguments"] + "\n```<|tool▁call▁end|>" -}} | |
| {%- else -%} | |
| {{- content + "<|tool▁calls▁begin|><|tool▁call▁begin|>" + tool["type"] + "<|tool▁sep|>" + tool["function"]["name"] + "\n```json\n" + tool["function"]["arguments"] + "\n```<|tool▁call▁end|>" -}} | |
| {%- endif -%} | |
| {%- set ns.is_first = true -%} | |
| {%- else -%} | |
| {{- "\n<|tool▁call▁begin|>" + tool["type"] + "<|tool▁sep|>" + tool["function"]["name"] + "\n```json\n" + tool["function"]["arguments"] + "\n```<|tool▁call▁end|>" -}} | |
| {%- endif -%} | |
| {%- endfor -%} | |
| {{- "<|tool▁calls▁end|><|end▁of▁sentence|>" -}} | |
| {%- endif -%} | |
| {%- if message["role"] == "assistant" and (message["tool_calls"] is not defined or message["tool_calls"] is none) -%} | |
| {%- set ns.is_last_user = false -%} | |
| {%- if ns.is_tool -%} | |
| {{- "<|tool▁outputs▁end|>" + content + "<|end▁of▁sentence|>" -}} | |
| {%- set ns.is_tool = false -%} | |
| {%- else -%} | |
| {{- content + "<|end▁of▁sentence|>" -}} | |
| {%- endif -%} | |
| {%- endif -%} | |
| {%- if message["role"] == "tool" -%} | |
| {%- set ns.is_last_user = false -%} | |
| {%- set ns.is_tool = true -%} | |
| {%- if ns.is_output_first -%} | |
| {{- "<|tool▁outputs▁begin|><|tool▁output▁begin|>" + content + "<|tool▁output▁end|>" -}} | |
| {%- set ns.is_output_first = false -%} | |
| {%- else -%} | |
| {{- "\n<|tool▁output▁begin|>" + content + "<|tool▁output▁end|>" -}} | |
| {%- endif -%} | |
| {%- endif -%} | |
| {%- endfor -%} | |
| {%- if ns.is_tool -%} | |
| {{- "<|tool▁outputs▁end|>" -}} | |
| {%- endif -%} | |
| {#- if add_generation_prompt and not ns.is_last_user and not ns.is_tool #} | |
| {%- if add_generation_prompt and not ns.is_tool %} | |
| {{- "<|Assistant|>" -}} | |
| {%- endif -%} |