Text Generation
Transformers
Safetensors
qwen2
control-foundation-model
scientific-ai
methodology-review
peer-review
rlvr
morphmind
conversational
text-generation-inference
Instructions to use MorphMind-AI/CFM-Methods-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MorphMind-AI/CFM-Methods-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MorphMind-AI/CFM-Methods-7B") 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("MorphMind-AI/CFM-Methods-7B") model = AutoModelForCausalLM.from_pretrained("MorphMind-AI/CFM-Methods-7B", 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 MorphMind-AI/CFM-Methods-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MorphMind-AI/CFM-Methods-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MorphMind-AI/CFM-Methods-7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/MorphMind-AI/CFM-Methods-7B
- SGLang
How to use MorphMind-AI/CFM-Methods-7B 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 "MorphMind-AI/CFM-Methods-7B" \ --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": "MorphMind-AI/CFM-Methods-7B", "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 "MorphMind-AI/CFM-Methods-7B" \ --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": "MorphMind-AI/CFM-Methods-7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use MorphMind-AI/CFM-Methods-7B with Docker Model Runner:
docker model run hf.co/MorphMind-AI/CFM-Methods-7B
Commit History
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Update benchmark figure (no truncation)
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Refresh card: clean two-panel benchmark figure, worked example, multi-domain framing
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Update weights: v2 conformance-optimized checkpoint (held-out R 0.984, FPR 0.000)
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Upload benchmark.png with huggingface_hub
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Add SOTA comparison: matches frontier recall/localization, 0 false alarms vs Opus 0.28 / GPT-4o 0.47
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Positive MorphMind framing
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CFM-Methods-7B: methodology screener (0.95 held-out recall, 0.95 loc, 0.00 FPR)
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initial commit
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