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AI Models & Assistants / Knowledge library
dolphin-2.9.1-yi-1.5-34b
text-generation model from dphn. Draft text, answer questions, or generate code with locally deployable weights.
Why learn dolphin-2.9.1-yi-1.5-34b?
Provides downloadable weights for text-generation experimentation, subject to the model card and license.
The problem it solves
Draft text, answer questions, or generate code with locally deployable weights.
Where it came from
dolphin-2.9.1-yi-1.5-34b: text-generation model from dphn. Draft text, answer questions, or generate code with locally deployable weights. License: apache-2.0. Hugging Face downloads at snapshot: 4671912. This is a task-stratified popularity sample, not a performance ranking. Runtime compatibility must be checked for the specific architecture.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Explain what dolphin-2.9.1-yi-1.5-34b does.
- Identify a use case for dolphin-2.9.1-yi-1.5-34b.
- Describe a limitation of dolphin-2.9.1-yi-1.5-34b.
- Name a compatible tool for dolphin-2.9.1-yi-1.5-34b.
- Compare dolphin-2.9.1-yi-1.5-34b against an alternative.
Documentation and sources
- Primary source / model card (huggingface.co)
Catalog research notes
- See the AI Workbench for source dates, pricing evidence and configuration details.
Connect the ideas
Built on
Real World Example
Automated Technical Support Agent for Legacy Systems
Enterprise software maintenance firms managing complex, undocumented legacy codebases.
The business problem
Support teams struggle with high-volume, repetitive technical queries that require deep reasoning and code-aware responses, leading to slow resolution times and developer burnout.
How this topic solves it
Deploying the Dolphin-2.9.1-yi-1.5-34b model allows for high-performance, instruction-tuned responses that leverage the model's inherent coding capabilities and function-calling support to automate initial triage and provide accurate technical guidance.
- Configure the inference environment to support the ChatML prompt template format required by the model.
- Implement a custom alignment layer to filter model outputs before they reach end-users, as the model is uncensored.
- Integrate the model with internal documentation and code repositories using function-calling to ground responses in specific system context.
- Validate the model's reasoning performance on domain-specific technical tasks using the 8k sequence length capacity.
The flow shows how user queries are processed through the model and filtered for safety.
Generates technical response
Filters output for safety
Final safe output
- User Query → Prompt → Dolphin-2.9.1
- Dolphin-2.9.1 → Raw Output → Alignment Layer
- Alignment Layer → Sanitized Output → Client Response
Implementation example
This demonstrates the required ChatML prompt template format used to interface with the model.
# Example of ChatML structure for the model
prompt = "<|im_start|>system\nYou are a technical assistant.<|im_end|>\n<|im_start|>user\nHow do I refactor this legacy function?<|im_end|>\n<|im_start|>assistant\n"Expected business outcome
Reduced manual triage burden on senior engineers and faster, consistent technical support responses for clients.
Limitations and checks
The model is uncensored, meaning it will be highly compliant with any requests, even unethical ones, which necessitates rigorous output monitoring to ensure safe deployment.