ThinkFL-Llama3.2-3B

6
llama
by
Zhang-Lingzhe
Other
OTHER
3B params
New
6 downloads
Early-stage
Edge AI:
Mobile
Laptop
Server
7GB+ RAM
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
3GB+ RAM

Training Data Analysis

🟡 Average (4.8/10)

Researched training datasets used by ThinkFL-Llama3.2-3B with quality assessment

Specialized For

general
science
multilingual
reasoning

Training Datasets (4)

common crawl
🔴 2.5/10
general
science
Key Strengths
  • Scale and Accessibility: At 9.5+ petabytes, Common Crawl provides unprecedented scale for training d...
  • Diversity: The dataset captures billions of web pages across multiple domains and content types, ena...
  • Comprehensive Coverage: Despite limitations, Common Crawl attempts to represent the broader web acro...
Considerations
  • Biased Coverage: The crawling process prioritizes frequently linked domains, making content from dig...
  • Large-Scale Problematic Content: Contains significant amounts of hate speech, pornography, violent c...
c4
🔵 6/10
general
multilingual
Key Strengths
  • Scale and Accessibility: 750GB of publicly available, filtered text
  • Systematic Filtering: Documented heuristics enable reproducibility
  • Language Diversity: Despite English-only, captures diverse writing styles
Considerations
  • English-Only: Limits multilingual applications
  • Filtering Limitations: Offensive content and low-quality text remain despite filtering
wikipedia
🟡 5/10
science
multilingual
Key Strengths
  • High-Quality Content: Wikipedia articles are subject to community review, fact-checking, and citatio...
  • Multilingual Coverage: Available in 300+ languages, enabling training of models that understand and ...
  • Structured Knowledge: Articles follow consistent formatting with clear sections, allowing models to ...
Considerations
  • Language Inequality: Low-resource language editions have significantly lower quality, fewer articles...
  • Biased Coverage: Reflects biases in contributor demographics; topics related to Western culture and ...
arxiv
🟡 5.5/10
science
reasoning
Key Strengths
  • Scientific Authority: Peer-reviewed content from established repository
  • Domain-Specific: Specialized vocabulary and concepts
  • Mathematical Content: Includes complex equations and notation
Considerations
  • Specialized: Primarily technical and mathematical content
  • English-Heavy: Predominantly English-language papers

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

json
[
  {
    "role": "system",
    "content": "You are a software operations engineer. Your task is to systematically diagnose and identify the root cause of software failures.\n\nYou have the following tools:\n\n[{\"type\": \"function\", \"function\": {\"name\": \"search_traces\", \"description\": \"Get all traces with the input span_id as the parent\", \"parameters\": {\"type\": \"object\", \"properties\": {\"parent_span_id\": {\"type\": \"string\", \"description\": \"The parent span_id to be queried.\"}}, \"required\": [\"parent_span_id\"]}}}, {\"type\": \"function\", \"function\": {\"name\": \"search_fluctuating_metrics\", \"description\": \"Get all fluctuating metrics with the input service_name and around the input timestamp\", \"parameters\": {\"type\": \"object\", \"properties\": {\"service_name\": {\"type\": \"string\", \"description\": \"The service_name to be queried.\"}, \"timestamp\": {\"type\": \"string\", \"description\": \"The timestamp to be queried.\"}}, \"required\": [\"service_name\", \"timestamp\"]}}}, {\"type\": \"function\", \"function\": {\"name\": \"print_results\", \"description\": \"Print the root cause node/service/pod and corresponding reason.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"root_causes\": {\"type\": \"array\", \"description\": \"An array of potential root causes ordered by likelihood. You may include any number of causes, but you must provide at least three potential root causes.\", \"items\": {\"type\": \"object\", \"properties\": {\"node\": {\"type\": \"string\", \"description\": \"The root cause node (e.g. node-1).\"}, \"service\": {\"type\": \"string\", \"description\": \"The root cause service (e.g. recommendationservice).\"}, \"pod\": {\"type\": \"string\", \"description\": \"The root cause pod (e.g. recommendationservice-0).\"}}, \"required\": []}}}}}}]\n\nThe parameters for these tools should be filled as a JSON object, with JSON keys as parameter names and key values as parameter content.\n\nYou must insert the following command once in your reply to call the tool:\n\n<tool_call>{'name': tool name, **can and must only** be one of [search_traces, search_fluctuating_metrics, print_results], 'arguments': tool input}<tool_call>"
  },
  {
    "role": "user",
    "content": "Please read the following root trace and identify corresponding root cause service.\nA possible approach is to recursively search for the traces you suspect have issues, and combine with metrics data to confirm the root cause.\n                             timestamp      cmdb_id           span_id                          trace_id  duration  type status_code              operation_name parent_span\n5159784  1647758897785   frontend-2  a260c8c867cf4b1c  f25cf75421090eef9c6c14270f1eb385  17244804  http           0  hipstershop.Frontend/Recv.         NaN"
  },
  {
    "role": "assistant",
    "content": "<tool_call>{\"name\": \"search_traces\", \"arguments\": \"{\\\"parent_span_id\\\": \\\"a260c8c867cf4b1c\\\"}\"}</tool_call>"
  },
  {
    "role": "tool",
    "content": "[{\"cmdb_id\":\"frontend-2\",\"duration\":17132774,\"operation_name\":\"hipstershop.CartService/GetCart\",\"parent_span\":\"a260c8c867cf4b1c\",\"span_id\":\"91486122a04fec40\",\"status_code\":\"0\",\"timestamp\":1647758897795,\"trace_id\":\"f25cf75421090eef9c6c14270f1eb385\",\"type\":\"rpc\"}, ...]"
  },
  {
    "role": "assistant",
    "content": "<tool_call>{\"name\": \"search_traces\", \"arguments\": \"{\\\"parent_span_id\\\": \\\"91486122a04fec40\\\"}\"}</tool_call>"
  },
  {
    "role": "tool",
    "content": "[{\"cmdb_id\":\"cartservice-2\",\"duration\":16177000,\"operation_name\":\"/hipstershop.CartService/GetCart\",\"parent_span\":\"91486122a04fec40\",\"span_id\":\"d55ed143102a3146\",\"status_code\":\"200\",\"timestamp\":1647758898844,\"trace_id\":\"f25cf75421090eef9c6c14270f1eb385\",\"type\":\"http\"}]"
  },
  {
    "role": "assistant",
    "content": "<tool_call>{\"name\": \"search_traces\", \"arguments\": \"{\\\"parent_span_id\\\": \\\"d55ed143102a3146\\\"}\"}</tool_call>"
  },
  {
    "role": "tool",
    "content": "[{\"cmdb_id\":\"cartservice-2\",\"duration\":2000,\"operation_name\":\"HGET\",\"parent_span\":\"d55ed143102a3146\",\"span_id\":\"f3d07142bfe0404b\",\"status_code\":\"Ok\",\"timestamp\":1647758914868,\"trace_id\":\"f25cf75421090eef9c6c14270f1eb385\",\"type\":\"db\"}]"
  },
  {
    "role": "assistant",
    "content": "<tool_call>{\"name\": \"search_fluctuating_metrics\", \"arguments\": \"{\\\"service_name\\\": \\\"cartservice\\\", \\\"timestamp\\\": \\\"1647758897785\\\"}\"}</tool_call>"
  },
  {
    "role": "tool",
    "content": "{\"node-5.cartservice2-0.container_cpu_cfs_throttled_periods\": {\"regular_mean\": 0.2, ...}, \"node-6.cartservice-2.container_cpu_usage_seconds\": {\"regular_mean\": 0.492..., \"current_mean\": 0.547...}, ...}"
  },
  {
    "role": "assistant",
    "content": "<tool_call>{\"name\": \"print_results\", \"arguments\": \"{\\\"root_causes\\\": [{\\\"service\\\": \\\"cartservice\\\"}, {\\\"pod\\\": \\\"cartservice-2\\\"}, {\\\"node\\\": \\\"node-6\\\"}, {\\\"service\\\": \\\"redis-cart\\\"}, {\\\"service\\\": \\\"productcatalog-cart\\\"}, {\\\"pod\\\": \\\"cartservice-2\\\"}, {\\\"service\\\": \\\"cartservice\\\"}, {\\\"node\\\": \\\"node-6\\\"}, {\\\"service\\\": \\\"cartservice\\\"}, {\\\"service\\\": \\\"recommendationservice\\\"}, {\\\"service\\\": \\\"adservice\\\"}, {\\\"service\\\": \\\"productcatalogservice\\\"}, {\\\"service\\\": \\\"currencyservice\\\"}]}\"}<tool_call>"
  }
]

Deploy This Model

Production-ready deployment in minutes

Together.ai

Instant API access to this model

Fastest API

Production-ready inference API. Start free, scale to millions.

Try Free API

Replicate

One-click model deployment

Easiest Setup

Run models in the cloud with simple API. No DevOps required.

Deploy Now

Disclosure: We may earn a commission from these partners. This helps keep LLMYourWay free.