What are the hardware requirements for Axolotl Unsloth Qwen Code 2p5 7B Lora 256 Sft?

Axolotl Unsloth Qwen Code 2p5 7B Lora 256 Sft requires approximately 15.2 GB of VRAM and supports a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Axolotl Unsloth Qwen Code 2p5 7B Lora 256 Sft and how large is it?

Axolotl Unsloth Qwen Code 2p5 7B Lora 256 Sft is developed by sourcegraph, a model with 7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate Axolotl Unsloth Qwen Code 2p5 7B Lora 256 Sft?

Axolotl Unsloth Qwen Code 2p5 7B Lora 256 Sft is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Axolotl Unsloth Qwen Code 2p5 7B Lora 256 Sft by sourcegraph — VRAM 15.2GB, 32K context

Name: Axolotl Unsloth Qwen Code 2p5 7B Lora 256 Sft
Author: sourcegraph

Axolotl Unsloth Qwen Code 2p5 7B Lora 256 Sft is an open-source language model by sourcegraph. Features: 7b LLM, VRAM: 15.2GB, Context: 32K, Code Generating, LLM Explorer Score: 0.19.

Codegen Lora Pytorch Qwen2 Region:us Sharded

Model Card on HF 🤗: https://huggingface.co/sourcegraph/axolotl-unsloth-qwen-code-2p5-7b-lora-256-sft

Axolotl Unsloth Qwen Code 2p5 7B Lora 256 Sft Benchmarks

LLME Score: 0.18559

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

What is the LLM Explorer Rank (Score)

Axolotl Unsloth Qwen Code 2p5 7B Lora 256 Sft (sourcegraph/axolotl-unsloth-qwen-code-2p5-7b-lora-256-sft)

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Axolotl Unsloth Qwen Code 2p5 7B Lora 256 Sft Parameters and Internals

LLM Name	Axolotl Unsloth Qwen Code 2p5 7B Lora 256 Sft
Repository 🤗	https://huggingface.co/sourcegraph/axolotl-unsloth-qwen-code-2p5-7b-lora-256-sft
Model Size	7b
Required VRAM	15.2 GB
Updated	2025-04-11
Maintainer	sourcegraph
Model Type	qwen2
Model Files	4.9 GB: 1-of-4 4.9 GB: 2-of-4 4.3 GB: 3-of-4 1.1 GB: 4-of-4
Generates Code	Yes
Model Architecture	Qwen2ForCausalLM
Context Length	32768
Model Max Length	32768
Transformers Version	4.46.3
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|PAD_TOKEN\|>
Vocabulary Size	152064
LoRA Model	Yes
Torch Data Type	float16
Errors	replace

Best Alternatives to Axolotl Unsloth Qwen Code 2p5 7B Lora 256 Sft

Best Alternatives	Context / RAM	Downloads	Likes
SakalFusion 7B Beta	986K / 15.2 GB	20	0
Qwen2.5 7B Rebase	986K / 15.2 GB	73	2
Qwen2.5 7B Rebase	986K / 15.2 GB	18	2
...R1 Distill Qwen MFANN Slerp 7B	128K / 15.2 GB	34	0
Qwen2.5 7B Coder Codeio Pp	128K / 15.2 GB	5	5
Qwen2.5 7B CySecButler V0.1	128K / 15.2 GB	5	3
CoT 2.5	128K / 15.2 GB	39	0
Mergekit Ties Uqhfast	128K / 15.2 GB	26	0
CoT 2.5	128K / 15.2 GB	26	0
Mergekit Ties Uqhfast	128K / 15.2 GB	13	0

Rank the Axolotl Unsloth Qwen Code 2p5 7B Lora 256 Sft Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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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Axolotl Unsloth Qwen Code 2p5 7B Lora 256 Sft by sourcegraph

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Axolotl Unsloth Qwen Code 2p5 7B Lora 256 Sft Benchmarks

Axolotl Unsloth Qwen Code 2p5 7B Lora 256 Sft Parameters and Internals

Best Alternatives to Axolotl Unsloth Qwen Code 2p5 7B Lora 256 Sft

Rank the Axolotl Unsloth Qwen Code 2p5 7B Lora 256 Sft Capabilities

What open-source LLMs or SLMs are you in search of? 53640 in total.