Text Generation
Transformers
Safetensors
English
mistral
chat
conversational
Eval Results (legacy)
text-generation-inference
Instructions to use anthracite-org/magnum-v4-22b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anthracite-org/magnum-v4-22b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="anthracite-org/magnum-v4-22b") 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("anthracite-org/magnum-v4-22b") model = AutoModelForCausalLM.from_pretrained("anthracite-org/magnum-v4-22b", 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 anthracite-org/magnum-v4-22b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "anthracite-org/magnum-v4-22b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "anthracite-org/magnum-v4-22b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/anthracite-org/magnum-v4-22b
- SGLang
How to use anthracite-org/magnum-v4-22b 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 "anthracite-org/magnum-v4-22b" \ --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": "anthracite-org/magnum-v4-22b", "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 "anthracite-org/magnum-v4-22b" \ --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": "anthracite-org/magnum-v4-22b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use anthracite-org/magnum-v4-22b with Docker Model Runner:
docker model run hf.co/anthracite-org/magnum-v4-22b
Download model-00002-of-00009.safetensors from anthracite-org/magnum-v4-22b: direct link, hf CLI and curl.
- Browser
- Download file 4.98 GB
-
https://huggingface.co/anthracite-org/magnum-v4-22b/resolve/main/model-00002-of-00009.safetensors
- Command line
-
hf download hf://anthracite-org/magnum-v4-22b/model-00002-of-00009.safetensors
-
curl -L -o model-00002-of-00009.safetensors https://huggingface.co/anthracite-org/magnum-v4-22b/resolve/main/model-00002-of-00009.safetensors
4.98 GB
- Xet hash:
- 7075c1e086a697afec0331a7c00a9cfbf1d172434ad12296ac0709787b3a04e7
- Size of remote file:
- 4.98 GB
- SHA256:
- b22ef060e4e15a48b292b08b9cac611e34a67ab79ea8573975631f46498a3852
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