Text Generation
Transformers
Safetensors
PyTorch
mistral
finetuned
quantized
4-bit precision
AWQ
instruct
conversational
text-generation-inference
finetune
chatml
DPO
RLHF
gpt4
synthetic data
distillation
awq
Instructions to use solidrust/bagel-7b-v0.5-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use solidrust/bagel-7b-v0.5-AWQ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="solidrust/bagel-7b-v0.5-AWQ") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("solidrust/bagel-7b-v0.5-AWQ") model = AutoModelForCausalLM.from_pretrained("solidrust/bagel-7b-v0.5-AWQ", device_map="auto") 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 solidrust/bagel-7b-v0.5-AWQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "solidrust/bagel-7b-v0.5-AWQ" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "solidrust/bagel-7b-v0.5-AWQ", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/solidrust/bagel-7b-v0.5-AWQ
- SGLang
How to use solidrust/bagel-7b-v0.5-AWQ 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 "solidrust/bagel-7b-v0.5-AWQ" \ --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": "solidrust/bagel-7b-v0.5-AWQ", "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 "solidrust/bagel-7b-v0.5-AWQ" \ --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": "solidrust/bagel-7b-v0.5-AWQ", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use solidrust/bagel-7b-v0.5-AWQ with Docker Model Runner:
docker model run hf.co/solidrust/bagel-7b-v0.5-AWQ
Commit History
Update tokenizer_config.json 9290cdd verified
update link to DPO version 8ebe38e verified
add model avatar 8287421 verified
add model card 202f1f4 verified
adding quant config 2d76893
Shaun Prince commited on
add awq model 821b17d
Shaun Prince commited on