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Qwen2 1.5B Instruct Function Calling V1 by devanshamin

 »  All LLMs  »  devanshamin  »  Qwen2 1.5B Instruct Function Calling V1   URL Share it on

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  Adapter Base model:adapter:qwen/qwen2-... Base model:qwen/qwen2-1.5b-ins...   Conversational Dataset:devanshamin/gem-viggo-...   Finetuned   Generated from trainer   Instruct   Lora   Peft   Qwen2   Region:us   Safetensors   Sft   Sharded   Tensorflow   Trl
Model Card on HF 🤗: https://huggingface.co/devanshamin/Qwen2-1.5B-Instruct-Function-Calling-v1 

Qwen2 1.5B Instruct Function Calling V1 Benchmarks

LLME Score: 0.20245
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Qwen2 1.5B Instruct Function Calling V1 (devanshamin/Qwen2-1.5B-Instruct-Function-Calling-v1)

Qwen2 1.5B Instruct Function Calling V1 Parameters and Internals

Model Type 
text-generation
Additional Notes 
The model adds a chat template to the tokenizer configuration that supports multiple tools, but the fine-tuning was specifically on a dataset that uses a single tool. Various usage examples including tool usage for function calling have been provided.
Training Details 
Data Sources:
devanshamin/gem-viggo-function-calling
LLM NameQwen2 1.5B Instruct Function Calling V1
Repository 🤗https://huggingface.co/devanshamin/Qwen2-1.5B-Instruct-Function-Calling-v1 
Base Model(s)  Qwen/Qwen2-1.5B-Instruct   Qwen/Qwen2-1.5B-Instruct
Model Size1.5b
Required VRAM6.2 GB
Updated2025-01-20
Maintainerdevanshamin
Instruction-BasedYes
Model Files  0.1 GB   5.0 GB: 1-of-2   1.2 GB: 2-of-2   0.0 GB
Model ArchitectureAdapter
Licenseapache-2.0
Model Max Length32768
Is Biasednone
Tokenizer ClassQwen2Tokenizer
Padding Token<|endoftext|>
PEFT TypeLORA
LoRA ModelYes
PEFT Target Modulesdown_proj|gate_proj|up_proj
LoRA Alpha64
LoRA Dropout0.05
R Param32
Errorsreplace

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Note: green Score (e.g. "73.2") means that the model is better than devanshamin/Qwen2-1.5B-Instruct-Function-Calling-v1.

Rank the Qwen2 1.5B Instruct Function Calling V1 Capabilities

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Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
Text Generation  
Text Summarization and Feature Extraction  
Code Generation  
Multi-Language Support and Translation  

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Release v20241227
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