Instructions to use meshllm/olmo-7b-instruct-hf-parity-bf16-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use meshllm/olmo-7b-instruct-hf-parity-bf16-mlx with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("meshllm/olmo-7b-instruct-hf-parity-bf16-mlx") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- MLX LM
How to use meshllm/olmo-7b-instruct-hf-parity-bf16-mlx with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "meshllm/olmo-7b-instruct-hf-parity-bf16-mlx"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "meshllm/olmo-7b-instruct-hf-parity-bf16-mlx" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "meshllm/olmo-7b-instruct-hf-parity-bf16-mlx", "messages": [ {"role": "user", "content": "Hello"} ] }'
OLMo-7B-Instruct-hf Parity BF16 MLX
Same-origin parity artifact derived from allenai/OLMo-7B-Instruct-hf.
This repo contains the high-fidelity bf16 MLX artifact used for mesh-llm backend parity validation against the corresponding GGUF artifact.
Accepted local validation status:
- Exact prompts: MLX now tracks GGUF on the semantic probe set after prompt-render parity fixes, with shared family-level drift on strict one-word canaries
- Behavior smoke: 0 flagged prompts out of 80 on the MT-Bench-derived harness
Paired GGUF repo:
meshllm/olmo-7b-instruct-hf-parity-f16-gguf
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Model size
7B params
Tensor type
BF16
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Hardware compatibility
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