Tinymistral V2 Pycoder Instruct 248M by jtatman

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Tinymistral V2 Pycoder Instruct 248M is an open-source language model by jtatman. Features: 248m LLM, VRAM: 1GB, Context: 32K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.11.

  Code Dataset:jtatman/pile python in... Dataset:jtatman/python-code-da... Dataset:jtatman/python-github-...   Endpoints compatible   Instruct   Mistral   Region:us   Safetensors

Tinymistral V2 Pycoder Instruct 248M Benchmarks

nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

Tinymistral V2 Pycoder Instruct 248M Parameters and Internals

Model Type 
MistralForCausalLM
Use Cases 
Primary Use Cases:
Generate python code.
Additional Notes 
Model is in active development, base model is in active development, and all should be treated with caution.
Training Details 
Data Sources:
jtatman/python-code-dataset-500k, jtatman/python-github-code-instruct-filtered-5k, jtatman/pile_python_instruct_format
Methodology:
Conversion to alpaca/instruct format.
LLM NameTinymistral V2 Pycoder Instruct 248M
Repository 🤗https://huggingface.co/jtatman/tinymistral-v2-pycoder-instruct-248m 
Model Size248m
Required VRAM1 GB
Updated2026-05-22
Maintainerjtatman
Model Typemistral
Instruction-BasedYes
Model Files  1.0 GB
Model ArchitectureMistralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.36.2
Tokenizer ClassLlamaTokenizer
Padding Token<|endoftext|>
Vocabulary Size32009
Torch Data Typefloat32

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Note: green Score (e.g. "73.2") means that the model is better than jtatman/tinymistral-v2-pycoder-instruct-248m.

Rank the Tinymistral V2 Pycoder Instruct 248M 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 v20260328a