Text Generation
Transformers
Safetensors
llama
alignment-handbook
simpo
Generated from Trainer
conversational
text-generation-inference
Instructions to use jackf857/llama-3-8b-base-simpo-8xh200 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jackf857/llama-3-8b-base-simpo-8xh200 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jackf857/llama-3-8b-base-simpo-8xh200") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("jackf857/llama-3-8b-base-simpo-8xh200") model = AutoModelForCausalLM.from_pretrained("jackf857/llama-3-8b-base-simpo-8xh200", 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 jackf857/llama-3-8b-base-simpo-8xh200 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jackf857/llama-3-8b-base-simpo-8xh200" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jackf857/llama-3-8b-base-simpo-8xh200", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/jackf857/llama-3-8b-base-simpo-8xh200
- SGLang
How to use jackf857/llama-3-8b-base-simpo-8xh200 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 "jackf857/llama-3-8b-base-simpo-8xh200" \ --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": "jackf857/llama-3-8b-base-simpo-8xh200", "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 "jackf857/llama-3-8b-base-simpo-8xh200" \ --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": "jackf857/llama-3-8b-base-simpo-8xh200", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use jackf857/llama-3-8b-base-simpo-8xh200 with Docker Model Runner:
docker model run hf.co/jackf857/llama-3-8b-base-simpo-8xh200
Download eval_results.json from jackf857/llama-3-8b-base-simpo-8xh200: direct link, hf CLI and curl.
- Browser
- Download file 585 Bytes
-
https://huggingface.co/jackf857/llama-3-8b-base-simpo-8xh200/resolve/main/eval_results.json
- Command line
-
hf download hf://jackf857/llama-3-8b-base-simpo-8xh200/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/jackf857/llama-3-8b-base-simpo-8xh200/resolve/main/eval_results.json
585 Bytes
| { | |
| "epoch": 0.9989528795811519, | |
| "eval_logits/chosen": -0.7624644637107849, | |
| "eval_logits/rejected": -0.7490274310112, | |
| "eval_logps/chosen": -1.5084009170532227, | |
| "eval_logps/rejected": -2.0472562313079834, | |
| "eval_loss": 1.0240256786346436, | |
| "eval_rewards/accuracies": 0.7419354915618896, | |
| "eval_rewards/chosen": -3.0168018341064453, | |
| "eval_rewards/margins": 1.0777103900909424, | |
| "eval_rewards/rejected": -4.094512462615967, | |
| "eval_runtime": 24.4713, | |
| "eval_samples": 2000, | |
| "eval_samples_per_second": 81.728, | |
| "eval_steps_per_second": 1.308 | |
| } |