Text Generation
Transformers
Safetensors
HERMES
Spanish
English
llama
llama-3.1
nemotron
andromeda
conversational
instruct
spanish
Merge
text-generation-inference
Instructions to use fiel1986/Andromeda-8B-Coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fiel1986/Andromeda-8B-Coder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="fiel1986/Andromeda-8B-Coder") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("fiel1986/Andromeda-8B-Coder") model = AutoModelForCausalLM.from_pretrained("fiel1986/Andromeda-8B-Coder", 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - HERMES
How to use fiel1986/Andromeda-8B-Coder with HERMES:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use fiel1986/Andromeda-8B-Coder with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "fiel1986/Andromeda-8B-Coder" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "fiel1986/Andromeda-8B-Coder", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/fiel1986/Andromeda-8B-Coder
- SGLang
How to use fiel1986/Andromeda-8B-Coder 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 "fiel1986/Andromeda-8B-Coder" \ --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": "fiel1986/Andromeda-8B-Coder", "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 "fiel1986/Andromeda-8B-Coder" \ --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": "fiel1986/Andromeda-8B-Coder", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use fiel1986/Andromeda-8B-Coder with Docker Model Runner:
docker model run hf.co/fiel1986/Andromeda-8B-Coder
Download tokenizer_config.json from fiel1986/Andromeda-8B-Coder: direct link, hf CLI and curl.
- Browser
- Download file 744 Bytes
-
https://huggingface.co/fiel1986/Andromeda-8B-Coder/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://fiel1986/Andromeda-8B-Coder/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/fiel1986/Andromeda-8B-Coder/resolve/main/tokenizer_config.json
744 Bytes
| { | |
| "add_bos_token": true, | |
| "add_eos_token": false, | |
| "bos_token": "<|begin_of_text|>", | |
| "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}", | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": "<|eot_id|>", | |
| "legacy": false, | |
| "model_max_length": 131072, | |
| "pad_token": "<|eot_id|>", | |
| "padding_side": "right", | |
| "tokenizer_class": "LlamaTokenizerFast", | |
| "unk_token": null | |
| } |