Instructions to use jan-hq/Ichigo-llama3.1-s-instruct-v0.4-step-6000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jan-hq/Ichigo-llama3.1-s-instruct-v0.4-step-6000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jan-hq/Ichigo-llama3.1-s-instruct-v0.4-step-6000") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("jan-hq/Ichigo-llama3.1-s-instruct-v0.4-step-6000") model = AutoModelForCausalLM.from_pretrained("jan-hq/Ichigo-llama3.1-s-instruct-v0.4-step-6000") 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 jan-hq/Ichigo-llama3.1-s-instruct-v0.4-step-6000 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jan-hq/Ichigo-llama3.1-s-instruct-v0.4-step-6000" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jan-hq/Ichigo-llama3.1-s-instruct-v0.4-step-6000", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/jan-hq/Ichigo-llama3.1-s-instruct-v0.4-step-6000
- SGLang
How to use jan-hq/Ichigo-llama3.1-s-instruct-v0.4-step-6000 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 "jan-hq/Ichigo-llama3.1-s-instruct-v0.4-step-6000" \ --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": "jan-hq/Ichigo-llama3.1-s-instruct-v0.4-step-6000", "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 "jan-hq/Ichigo-llama3.1-s-instruct-v0.4-step-6000" \ --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": "jan-hq/Ichigo-llama3.1-s-instruct-v0.4-step-6000", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use jan-hq/Ichigo-llama3.1-s-instruct-v0.4-step-6000 with Docker Model Runner:
docker model run hf.co/jan-hq/Ichigo-llama3.1-s-instruct-v0.4-step-6000
- Xet hash:
- 27e867c2e3fd74e39d66c0a1bd2447ce54c23ceb7d4433f16c5ebea94085e823
- Size of remote file:
- 17.3 MB
- SHA256:
- 4cdfaca63479499a9a90392684b3b75fff1cc8d49a83387a81b581511a8abf93
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