Troubleshooting Deepseek-Coder-V2 on Ollama: A Community Discussion

The world of local Large Language Models (LLMs) is constantly evolving, and platforms like Ollama are making it easier than ever to experiment with powerful models on personal hardware. However, challenges can arise when trying to run specific models, as highlighted in a recent Reddit post on the r/ollama subreddit. This article delves into the issue of getting Deepseek-Coder-V2 to run on Ollama, exploring potential causes and solutions based on community discussions and technical insights.

The Issue: Aborted Context Creation

A user, "M3GaPrincess," reported encountering an error while attempting to run Deepseek-Coder-V2, specifically the deepseek-coder-v2:236b-instruct-q2_K variant. Despite having successfully run 101 GB models previously, they faced the following error:

Error: llama runner process has terminated: signal: aborted (core dumped) error: failed to create context with model '/var/lib/ollama/.ollama/models/blobs/sha256-99537d8560898b98fd47e78d7a11d6c90d775a3c78a452e324d196ddf4135205' It's similar to an OOM memory but on the context token?

The error message points towards a potential issue with creating the context for the model, possibly resembling an out-of-memory (OOM) error related to token handling. This is particularly puzzling given the user's prior success with other large models.

Potential Causes and Troubleshooting Steps

While a definitive solution requires more information about the user's system and Ollama configuration, here are some potential causes and troubleshooting steps to consider:

• Insufficient System Memory (RAM): While the user has run 101 GB models successfully, Deepseek-Coder-V2 might have different memory requirements during context creation. Monitor RAM usage during model loading to confirm.
• GPU Memory Limitations: Ollama can leverage the GPU for faster inference. Ensure your GPU has enough memory to accommodate the model and its context. Lowering the quantization level (e.g., trying a q4_K or q5_K variant) can reduce memory footprint.
• Ollama Version Incompatibility: An outdated version of Ollama might have compatibility issues with specific models or quantization formats. Ensure you're running the latest version of Ollama. Check Ollama's official website for the latest releases and update instructions.
• Corrupted Model File: Although less likely, it's possible that the downloaded model file is corrupted. Try re-downloading the model.
• Operating System Limitations: Certain operating systems might have limitations on memory allocation or process size. Research OS-specific limitations.
• Context Window Size: Examine if the model has requirements on the context window size.
• Conflicting Software: Other software running in the background can sometimes interfere with Ollama's operation or consume resources. Try closing unnecessary applications to free up resources.

Further Investigation and Community Support

• Ollama Logs: Examine Ollama's logs for more detailed error messages or clues about the failure. The log location may vary based on the operating system.
• Community Forums: Engage with the Ollama community on platforms like Reddit (r/ollama) or the [Ollama Discord server](link to Ollama Discord if available) to seek assistance and share your troubleshooting steps.
• System Specifications: Providing detailed system specifications (CPU, GPU, RAM, operating system) when seeking help can enable more targeted advice.

While the r/ollama post doesn't offer a definitive solution in the immediate comments, it serves as a valuable starting point for troubleshooting and highlights the importance of community collaboration in resolving technical challenges within the LLM space. By systematically investigating potential causes and leveraging community resources, users can increase their chances of successfully running Deepseek-Coder-V2 and other powerful models on Ollama.