The Ollama project has made significant strides in enhancing model handling and API usability, but persistent resource management issues and model handling errors pose notable risks to its trajectory.
Daniel Hiltgen (dhiltgen)
Summary
Detailed Reports
llm/server.go (+16, -12)scripts/install.sh (+1, -1)Michael Yang (mxyng)
llm/ggml.go (+33, -7), llm/memory.go (+2, -2)Josh (joshyan1)
gpu/assets.go (+19, -12)Roy Han (royjhan)
api/types.go, cmd/cmd.goThe team demonstrates strong collaboration with frequent cross-reviews and integration of work across different aspects of the project. Key contributors are actively involved in various areas, showcasing a dynamic and collaborative workflow.
The recent activity underscores a robust phase of development. Ongoing enhancements in model handling, API usability, and system compatibility indicate a positive trajectory. However, addressing resource management issues and improving model handling will be crucial for sustained growth.
Severity: Medium
Ongoing challenges with GPU handling have been reported, affecting performance and reliability.
use_mmap ParameterSeverity: Medium
Errors with the use_mmap parameter in version 0.1.45 suggest potential regression or bugs.
use_mmap parameter.Severity: Medium
Errors have been reported while running the quwen2-instruct-70b model, indicating potential compatibility or runtime environment issues.
quwen2-instruct-70b model.Severity: Medium
Inconsistent results with seeded API requests affect reproducibility.
#5196: Include Modelfile Messages
#5193: Correct Ollama Show Precision of Parameter
#5191: Adding Introduction of x-cmd/ollama Module
#5194: Refine mmap Default Logic on Linux
#5192: Handle Asymmetric Embedding KVs
#5188: Fix os.removeAll() if PID Does Not Exist
The Ollama project is making significant progress but must address resource management issues and improve model handling to maintain its positive trajectory. Active collaboration among contributors is a strong asset that should be leveraged to tackle these challenges effectively.
| Developer | Avatar | Branches | PRs | Commits | Files | Changes |
|---|---|---|---|---|---|---|
| Jeffrey Morgan |  |
3 | 4/5/0 | 12 | 221 | 4719 |
| vs. last report | -2 | -4/+1/-1 | -33 | -25 | -180342 | |
| Michael Yang |  |
3 | 6/3/0 | 7 | 10 | 1759 |
| vs. last report | +1 | -3/-7/= | -4 | -4 | +1066 | |
| royjhan |  |
9 | 6/2/1 | 19 | 14 | 852 |
| vs. last report | +2 | =/+1/+1 | -1 | +7 | -27 | |
| Daniel Hiltgen |  |
1 | 16/16/0 | 14 | 23 | 689 |
| vs. last report | = | +12/+14/= | +12 | +20 | +684 | |
| Blake Mizerany |  |
1 | 1/1/0 | 1 | 2 | 89 |
| Wang, Zhe |  |
1 | 1/1/0 | 2 | 3 | 63 |
| Josh |  |
1 | 2/1/1 | 3 | 1 | 43 |
| Lei Jitang |  |
1 | 2/2/0 | 2 | 1 | 4 |
| vs. last report | +1 | +1/+2/= | +2 | +1 | +4 | |
| Patrick Devine |  |
1 | 1/1/0 | 1 | 1 | 2 |
| vs. last report | = | -2/-2/= | -2 | -13 | -280 | |
| Sam (sammcj) | 0 | 1/0/0 | 0 | 0 | 0 | |
| dcasota (dcasota) | 0 | 1/0/0 | 0 | 0 | 0 | |
| vs. last report | -1 | +1/-1/-1 | -1 | -1 | -23 | |
| Ibraheem Mobolaji Abdulsalam (moriire) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Plamen Mushkov (plamen9) | 0 | 1/0/0 | 0 | 0 | 0 | |
| None (crazy2be) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Milkey Tan (mili-tan) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Noufal Ibrahim (nibrahim) | 0 | 1/0/0 | 0 | 0 | 0 | |
| vs. last report | = | =/=/= | = | = | = | |
| Vyacheslav (slavonnet) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Edwin.JH.Lee (edwinjhlee) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Silas Marvin (SilasMarvin) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Jakob (jakobdylanc) | 0 | 1/0/0 | 0 | 0 | 0 | |
| pufferfish (pufferffish) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Sumingcheng (sumingcheng) | 0 | 2/0/1 | 0 | 0 | 0 | |
| 苏业钦 (HougeLangley) | 0 | 1/0/1 | 0 | 0 | 0 | |
| JD Davis (JerrettDavis) | 0 | 3/0/1 | 0 | 0 | 0 | |
| vs. last report | = | +2/=/= | = | = | = | |
| Elliot (elliotwellick) | 0 | 0/0/1 | 0 | 0 | 0 | |
| None (jayson-cloude) | 0 | 0/1/0 | 0 | 0 | 0 | |
| vs. last report | = | -1/+1/= | = | = | = | |
| Where data meets intelligence (perpendicularai) | 0 | 1/0/0 | 0 | 0 | 0 |
PRs: created by that dev and opened/merged/closed-unmerged during the period
The "ollama" project is a software initiative focused on providing tools and functionalities for managing and utilizing large language models in local environments. The project appears to be under active development with contributions from a dedicated team of developers. While the responsible organization is not explicitly mentioned, the active involvement of multiple contributors suggests a collaborative effort, possibly open-source. The project's current state shows robust activity with ongoing enhancements in model handling, API usability, and system compatibility, indicating a positive trajectory towards further growth and innovation.
Daniel Hiltgen (dhiltgen)
llm/server.go (+16, -12)scripts/install.sh (+1, -1)llm/server.go (+16, -12)Michael Yang (mxyng)
llm/ggml.go (+33, -7), llm/memory.go (+2, -2)llm/ggml.go (+33, -7), llm/memory.go (+2, -2)Josh (joshyan1)
gpu/assets.go (+19, -12)gpu/assets.go (+12, -8)gpu/assets.go (+11, -10)gpu/assets.go (+2, -0)Roy Han (royjhan)
api/types.go, cmd/cmd.go, etc.Daniel Hiltgen (dhiltgen)
app/lifecycle/logging.go, envconfig/config.go, etc.Michael Yang (mxyng)
llm/ext_server/server.cpp, llm/ggml.go.Wang, Zhe (zhewang1-intc)
gpu/gpu_info_oneapi.c, envconfig/config.go.Blake Mizerany (bmizerany)
types/model/name.go, types/model/name_test.go.Jeffrey Morgan (jmorganca)
– Commit: Update import.md.
* Description: Minor updates to import documentation.
* Files: Various including docs/import.md.
The development team exhibits strong collaboration patterns with frequent cross-reviews and integration of work across different aspects of the project. The use of multiple branches for specific features or fixes indicates a well-organized approach to managing new developments without disrupting the main codebase. Key contributors like Jeffrey Morgan, Michael Yang, Roy Han, Josh Yan, and others are actively involved in various aspects of the project, showcasing a dynamic and collaborative workflow.
The recent flurry of activity underscores a robust phase of development for the ollama project. With ongoing enhancements in model handling, API usability, and system compatibility, the project is poised for further growth. The active involvement from both core developers and community contributors is a positive sign for the project's sustainability and innovation. Given the current trajectory, it is expected that further enhancements will continue to roll out, potentially introducing new features or expanding the range of compatible models and systems. This ongoing development effort is likely to further cement ollama's position as a valuable tool for developers looking to leverage large language models in a local environment.
Since the last report, there has been significant activity in the Ollama project. This includes the opening of several new issues, updates to existing issues, and some issues being closed. The newly opened issues highlight various problems, enhancement requests, and user queries.
use_mmap parameter in version 0.1.45, indicating a potential regression or bug in handling boolean values.quwen2-instruct-70b model, which could be related to model compatibility or runtime environment issues.ollama show, addressing issue #5184.ollama show, addressing issue #5183./api/chat endpoint, indicating potential compatibility issues with chat templates.ollama show, addressing precision concerns.ollama show has quotes around stop words, suggesting a need for formatting improvements.mxbai-embed-large model, suggesting gaps in parameter support.Resource Management Issues:
Model Import and Usage Issues:
Internet Connectivity Sensitivity:
The recent activity within the Ollama project indicates active engagement from both maintainers and the community. While new features and improvements are being proposed and implemented, there are areas such as resource management, model handling, and internet connectivity that require ongoing attention to ensure reliability and usability. The quick closure of several issues also reflects well on the project's maintenance processes.
This pull request (PR) introduces changes to the server and API behavior in the ollama/ollama repository. Specifically, it moves the CLI behavior of prepending Modelfile messages to chat conversations to the server. This change allows API calls to easily use these fields and extends this functionality to /api/generate requests where messages will only be prepended if the context is empty.
cmd/interactive.go: Removed code that appends messages from showResp.Messages to opts.Messages.server/images.go: Changed the type of Messages in the Model struct from a local Message type to api.Message.server/prompt.go: Modified the chatPrompt function to prepend model messages to incoming messages.server/routes.go: Updated the GenerateHandler function to handle system prompts and context more effectively.cmd/interactive.go:
showResp.Messages to opts.Messages.server/images.go:
Messages field in the Model struct from a local type (Message) to an API type (api.Message).Message struct.server/prompt.go:
chatPrompt function to include model messages by default using:
go
msgs = slices.DeleteFunc(append(r.model.Messages, msgs...), func(m api.Message) bool {
if m.Role == "system" {
system = append(system, m)
return true
}
return false
})server/routes.go:
GenerateHandler function to handle system prompts and context more effectively:
go
if req.Context == nil {
msgs = append(msgs, r.model.Messages...)
}Clarity and Readability:
cmd/interactive.go improves readability.server/images.go simplifies type management by using a single message type (api.Message) across different parts of the codebase.Functionality:
server/routes.go is well-structured, ensuring that prompts are handled with clear precedence rules.Maintainability:
Testing:
The changes introduced in PR #5196 improve how Modelfile messages are handled across both CLI and API contexts, enhancing consistency and functionality. However, it is critical to address the missing tests before merging to ensure robust and reliable behavior.
Since the last report 7 days ago, there has been significant activity in the Ollama project's pull requests. Several new pull requests have been opened, and a number of them have been closed or merged. Below is a detailed analysis of the recent activity, highlighting notable changes and their implications for the project.
#5196: include modelfile messages
cmd/interactive.go, server/images.go, server/prompt.go, server/routes.go#5193: Correct Ollama Show Precision of Parameter
cmd/cmd.go, format/format.go, format/format_test.go#5191: Adding introduction of x-cmd/ollama module
README.md#5190: Remove Quotes from Parameters in Ollama Show
cmd/cmd.go#5151: Update OpenAI Compatibility Docs with /v1/models
docs/openai.md/v1/models.#5194: Refine mmap default logic on linux
llm/server.go#5192: handle asymmetric embedding KVs
llm/ggml.go, llm/memory.go#5188: fix: skip os.removeAll() if PID does not exist
gpu/assets.go#5147: remove confusing log message
llm/ext_server/server.cpp#5146: Put back temporary intel GPU env var
envconfig/config.go, gpu/gpu.go#5187: fix: skip os.removeAll() in assets.go if no PID
#5078: Add Chinese translation of README
The Ollama project has seen substantial activity over the past seven days with numerous PRs being opened and closed. The changes range from minor documentation updates to significant code improvements that enhance usability, performance, and maintainability. The project's active development and community engagement are evident from these updates.
For future development, it will be important to continue focusing on stability improvements and addressing any remaining bugs promptly while also expanding community integrations and support for various platforms.
llm/server.goSummary: This file is central to server operations, particularly focusing on memory mapping (mmap) logic and memory prediction logging. It has been frequently updated, indicating its critical role in the system.
Analysis:
Recommendations:
llm/ggml.goSummary: This file handles asymmetric embedding KVs and deepseek v2 graph, indicating its importance in model handling.
Analysis:
GGML, KV, Tensors, and Tensor are defined with specific methods, enhancing readability.DecodeGGML returns detailed errors which can be useful for debugging.GraphSize calculate memory requirements efficiently using batch processing techniques.model, container) allows for flexible implementations and easier testing.Recommendations:
gpu/assets.goSummary: This file manages GPU assets, focusing on error checking and PID handling.
Analysis:
sync.Mutex) to handle concurrent access to shared resources (payloadsDir), ensuring thread safety.PayloadsDir and cleanupTmpDirs.slog) effectively for debugging and monitoring.Recommendations:
api/types.goSummary: Defines API types and structures, crucial for API interactions and data exchange.
Analysis:
GenerateRequest, ChatRequest, ShowResponse).TriState and Duration, ensuring correct data representation.Options struct encapsulates various model-specific options, providing flexibility in API usage.StatusError type provides a structured way to handle HTTP status errors, improving error reporting.Recommendations:
cmd/cmd.goSummary: Handles command-line interface (CLI) operations, extending API show functionality and providing descriptive argument error messages.
Analysis:
Recommendations:
Severity: Medium (2/3)
Rationale
Ongoing challenges with resource management, particularly with GPU handling, have been reported. Users have indicated that Ollama is not utilizing GPUs effectively despite having CUDA and cuDNN installed, and multiple NVIDIA H100 GPUs are not being utilized efficiently.
Next Steps
use_mmap parameterSeverity: Medium (2/3)
Rationale
The use_mmap parameter has been reported to cause errors in version 0.1.45, indicating a potential regression or bug in handling boolean values.
use_mmap parameter.Next Steps
use_mmap parameter.Severity: Medium (2/3)
Rationale
Errors have been reported while running the quwen2-instruct-70b model, which could be related to model compatibility or runtime environment issues.
quwen2-instruct-70b model.Next Steps
quwen2-instruct-70b model.Severity: Medium (2/3)
Rationale
Inconsistent results have been reported when using seeded API requests with specific parameters. This inconsistency can affect reproducibility, which is critical for many applications.
Next Steps