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README.md
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---
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license: apache-2.0
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---
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## sample inference.
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[{"name": "passing_percentage", "description": "Calculates the passing percentage for an exam given the percentage of students who passed each subject, and the intersection percentage of passing subjects.",
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"parameters": {"subject1_percent": {"description": "Percentage of students who passed the first subject (e.g., 85% if Hindi).", "type": "int"},
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"subject2_percent": {"description": "Percentage of students who passed the second subject (e.g., 80% if Urdu).", "type": "int"}, "passed_both_percent": {"description": "Percentage of students who passed both subjects.", "type": "int"}}}]
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```
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---
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license: apache-2.0
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language:
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- en
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base_model:
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- prithivMLmods/Gliese-Query_Tool-0.6B
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- text-generation-inference
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- agent
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- tool_calling
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datasets:
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- Salesforce/xlam-function-calling-60k
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---
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# **Gliese-Query_Tool-0.6B**
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> Gliese-Query_Tool-0.6B is a function-calling and query-oriented reasoning model fine-tuned from Qwen3-0.6B using Salesforce/xlam-function-calling-60k, designed for tool orchestration, structured query resolution, and operation chaining across diverse tasks. It excels in dynamic function execution, structured reasoning pipelines, and multi-tool decision workflows, making it a powerful lightweight solution for developers, tooling platforms, and automation systems.
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## Model Files
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| File Name | Quant Type | File Size |
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| - | - | - |
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| Gliese-Query_Tool-0.6B.BF16.gguf | BF16 | 1.2 GB |
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| Gliese-Query_Tool-0.6B.F16.gguf | F16 | 1.2 GB |
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| Gliese-Query_Tool-0.6B.F32.gguf | F32 | 2.39 GB |
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| Gliese-Query_Tool-0.6B.Q2_K.gguf | Q2_K | 296 MB |
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| Gliese-Query_Tool-0.6B.Q3_K_L.gguf | Q3_K_L | 368 MB |
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| Gliese-Query_Tool-0.6B.Q3_K_M.gguf | Q3_K_M | 347 MB |
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| Gliese-Query_Tool-0.6B.Q3_K_S.gguf | Q3_K_S | 323 MB |
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| Gliese-Query_Tool-0.6B.Q4_0.gguf | Q4_0 | 382 MB |
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| Gliese-Query_Tool-0.6B.Q4_1.gguf | Q4_1 | 409 MB |
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| Gliese-Query_Tool-0.6B.Q4_K.gguf | Q4_K | 397 MB |
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| Gliese-Query_Tool-0.6B.Q4_K_M.gguf | Q4_K_M | 397 MB |
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| Gliese-Query_Tool-0.6B.Q4_K_S.gguf | Q4_K_S | 383 MB |
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| Gliese-Query_Tool-0.6B.Q5_0.gguf | Q5_0 | 437 MB |
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| Gliese-Query_Tool-0.6B.Q5_1.gguf | Q5_1 | 464 MB |
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| Gliese-Query_Tool-0.6B.Q5_K.gguf | Q5_K | 444 MB |
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| Gliese-Query_Tool-0.6B.Q5_K_M.gguf | Q5_K_M | 444 MB |
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| Gliese-Query_Tool-0.6B.Q5_K_S.gguf | Q5_K_S | 437 MB |
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| Gliese-Query_Tool-0.6B.Q6_K.gguf | Q6_K | 495 MB |
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| Gliese-Query_Tool-0.6B.Q8_0.gguf | Q8_0 | 639 MB |
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## sample inference.
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[{"name": "passing_percentage", "description": "Calculates the passing percentage for an exam given the percentage of students who passed each subject, and the intersection percentage of passing subjects.",
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"parameters": {"subject1_percent": {"description": "Percentage of students who passed the first subject (e.g., 85% if Hindi).", "type": "int"},
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"subject2_percent": {"description": "Percentage of students who passed the second subject (e.g., 80% if Urdu).", "type": "int"}, "passed_both_percent": {"description": "Percentage of students who passed both subjects.", "type": "int"}}}]
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```
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## Quants Usage
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(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
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Here is a handy graph by ikawrakow comparing some lower-quality quant
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types (lower is better):
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