OxW/Qwen2.5-0.5B-Instruct_Q8_0

OxW/ Qwen2.5-0.5B-Instruct_Q8_0

345 Downloads Updated 10 months ago

[GGUF] Qwen2.5 instruction-tuned language model ranging from 0.5 to 72 billion parameters.

tools

ollama run OxW/Qwen2.5-0.5B-Instruct_Q8_0

curl http://localhost:11434/api/chat \
  -d '{
    "model": "OxW/Qwen2.5-0.5B-Instruct_Q8_0",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

from ollama import chat

response = chat(
    model='OxW/Qwen2.5-0.5B-Instruct_Q8_0',
    messages=[{'role': 'user', 'content': 'Hello!'}],
)
print(response.message.content)

import ollama from 'ollama'

const response = await ollama.chat({
  model: 'OxW/Qwen2.5-0.5B-Instruct_Q8_0',
  messages: [{role: 'user', content: 'Hello!'}],
})
console.log(response.message.content)

Applications

Claude Code

Claude Code ollama launch claude --model OxW/Qwen2.5-0.5B-Instruct_Q8_0

Codex

Codex ollama launch codex --model OxW/Qwen2.5-0.5B-Instruct_Q8_0

OpenCode

OpenCode ollama launch opencode --model OxW/Qwen2.5-0.5B-Instruct_Q8_0

OpenClaw

OpenClaw ollama launch openclaw --model OxW/Qwen2.5-0.5B-Instruct_Q8_0

Models

Name

1 model

Size

Context

Input

Qwen2.5-0.5B-Instruct_Q8_0:latest

676MB · 32K context window · Text · 10 months ago

Qwen2.5-0.5B-Instruct_Q8_0:latest

676MB

32K

Text

Readme

Source: HF

Qwen2.5 brings the following improvements upon Qwen2:

Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains.
Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots.
Long-context Support up to 128K tokens and can generate up to 8K tokens.
Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.

This repo contains the instruction-tuned 0.5B Qwen2.5 model in the GGUF Format, which has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Architecture: transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings - Number of Parameters: 0.49B - Number of Paramaters (Non-Embedding): 0.36B - Number of Layers: 24 - Number of Attention Heads (GQA): 14 for Q and 2 for KV - Context Length: Full 32,768 tokens and generation 8192 tokens

For more details, please refer to our blog, GitHub, and Documentation.

Source: [HF](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct-GGUF)

Qwen2.5 brings the following improvements upon Qwen2:

- Significantly **more knowledge** and has greatly improved capabilities in **coding** and **mathematics**, thanks to our specialized expert models in these domains.
- Significant improvements in **instruction following**, **generating long texts** (over 8K tokens), **understanding structured data** (e.g, tables), and **generating structured outputs** especially JSON. **More resilient to the diversity of system prompts**, enhancing role-play implementation and condition-setting for chatbots.
- **Long-context Support** up to 128K tokens and can generate up to 8K tokens.
- **Multilingual support** for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.

**This repo contains the instruction-tuned 0.5B Qwen2.5 model in the GGUF Format**, which has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Architecture: transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings
- Number of Parameters: 0.49B
- Number of Paramaters (Non-Embedding): 0.36B
- Number of Layers: 24
- Number of Attention Heads (GQA): 14 for Q and 2 for KV
- Context Length: Full 32,768 tokens and generation 8192 tokens

For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5/), [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/).

Paste, drop or click to upload images (.png, .jpeg, .jpg, .svg, .gif)