Text Generation
Transformers
Safetensors
English
llama
astheral
instruct
qlora
conversational
text-generation-inference
Instructions to use SEE7/Astheral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SEE7/Astheral with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SEE7/Astheral") 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("SEE7/Astheral") model = AutoModelForCausalLM.from_pretrained("SEE7/Astheral", 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]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use SEE7/Astheral with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SEE7/Astheral" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SEE7/Astheral", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/SEE7/Astheral
- SGLang
How to use SEE7/Astheral 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 "SEE7/Astheral" \ --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": "SEE7/Astheral", "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 "SEE7/Astheral" \ --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": "SEE7/Astheral", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use SEE7/Astheral with Docker Model Runner:
docker model run hf.co/SEE7/Astheral
Download data_sources.json from SEE7/Astheral: direct link, hf CLI and curl.
- Browser
- Download file 1.95 kB
-
https://huggingface.co/SEE7/Astheral/resolve/main/data_sources.json
- Command line
-
hf download hf://SEE7/Astheral/data_sources.json
-
curl -L -o data_sources.json https://huggingface.co/SEE7/Astheral/resolve/main/data_sources.json
1.95 kB
| { | |
| "sources": [ | |
| { | |
| "repo": "HuggingFaceTB/smoltalk", | |
| "config": "smol-magpie-ultra", | |
| "revision": "5feaf2fd3ffca7c237fc38d1861bc30365d48ffa", | |
| "count": 650, | |
| "license": "Apache-2.0" | |
| }, | |
| { | |
| "repo": "HuggingFaceTB/smoltalk", | |
| "config": "smol-constraints", | |
| "revision": "5feaf2fd3ffca7c237fc38d1861bc30365d48ffa", | |
| "count": 350, | |
| "license": "Apache-2.0" | |
| }, | |
| { | |
| "repo": "HuggingFaceTB/smoltalk", | |
| "config": "smol-rewrite", | |
| "revision": "5feaf2fd3ffca7c237fc38d1861bc30365d48ffa", | |
| "count": 100, | |
| "license": "Apache-2.0" | |
| }, | |
| { | |
| "repo": "HuggingFaceTB/smoltalk", | |
| "config": "smol-summarize", | |
| "revision": "5feaf2fd3ffca7c237fc38d1861bc30365d48ffa", | |
| "count": 100, | |
| "license": "Apache-2.0" | |
| }, | |
| { | |
| "repo": "openai/gsm8k", | |
| "revision": "740312add88f781978c0658806c59bc2815b9866", | |
| "split": "train", | |
| "count": 400, | |
| "license": "MIT" | |
| }, | |
| { | |
| "repo": "bigcode/self-oss-instruct-sc2-exec-filter-50k", | |
| "revision": "356bb069eee815daa6e23e9a282eeefe1490ad44", | |
| "count": 250, | |
| "license": "ODC-BY-1.0" | |
| } | |
| ], | |
| "counts": { | |
| "HuggingFaceTB/smoltalk/smol-summarize": 100, | |
| "HuggingFaceTB/smoltalk/smol-rewrite": 100, | |
| "bigcode/self-oss-instruct-sc2-exec-filter-50k": 250, | |
| "HuggingFaceTB/smoltalk/smol-constraints": 350, | |
| "astheral-curriculum": 598, | |
| "openai/gsm8k": 400, | |
| "HuggingFaceTB/smoltalk/smol-magpie-ultra": 650, | |
| "astheral-authored-v1": 45 | |
| }, | |
| "total": 2493, | |
| "seed": 20261113, | |
| "repair": { | |
| "source_revision": "930a185fd9f4e7fffe44283fa9009ccbd2aa0af7", | |
| "initial_adapter": "repair-results/adapter", | |
| "previous_results": "repair-results", | |
| "repair_pass": 2, | |
| "new_curriculum_examples": 781, | |
| "train_examples_before_token_filter": 1617, | |
| "validation_examples": 64, | |
| "retention_examples": 900, | |
| "seed": 20261115 | |
| } | |
| } |