Text Generation
Transformers
Safetensors
qwen2
Generated from Trainer
trl
kto
conversational
text-generation-inference
Instructions to use SoonOk/SFTWeAgents with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SoonOk/SFTWeAgents with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SoonOk/SFTWeAgents") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("SoonOk/SFTWeAgents") model = AutoModelForCausalLM.from_pretrained("SoonOk/SFTWeAgents") 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 SoonOk/SFTWeAgents with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SoonOk/SFTWeAgents" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SoonOk/SFTWeAgents", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/SoonOk/SFTWeAgents
- SGLang
How to use SoonOk/SFTWeAgents 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 "SoonOk/SFTWeAgents" \ --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": "SoonOk/SFTWeAgents", "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 "SoonOk/SFTWeAgents" \ --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": "SoonOk/SFTWeAgents", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use SoonOk/SFTWeAgents with Docker Model Runner:
docker model run hf.co/SoonOk/SFTWeAgents
Ctrl+K
- Qwen14B_SFT_original
- Qwen14B_SFT_ours
- Qwen72B_SFT_original
- Qwen72B_SFT_ours
- Qwen7B_SFT_original
- Qwen7B_SFT_ours
- checkpoint-540
- checkpoint-580
- makto_v1
- ours_v1_3_260428
- ours_v3
- 2.48 kB
- 1.99 kB
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- 2.51 kB
- 1.32 kB
- 150 MB xet
- 217 Bytes
- 18 MB xet
- 30.4 MB xet
- 19 MB xet
- 81.9 MB xet
- 1.67 MB
- 4.88 GB xet
- 4.93 GB xet
- 4.33 GB xet
- 1.09 GB xet
- 27.8 kB
- 613 Bytes
- 11.4 MB xet
- 4.69 kB
- 29.9 kB
- 7.44 kB xet
- 2.78 MB