edtorre/qwen3.5-hermes

edtorre/ qwen3.5-hermes:latest

258 Downloads Updated 5 days ago

A compact, agent-tuned Qwen 3.5 model built for local agent harnesses like Hermes Agent. Based on `qwen3.5:latest` with Q4_K_M quantization for a lightweight 6.6 GB footprint, while retaining strong tool-calling, vision, and thinking capabilities.

vision tools thinking

ollama run edtorre/qwen3.5-hermes

curl http://localhost:11434/api/chat \
  -d '{
    "model": "edtorre/qwen3.5-hermes",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

from ollama import chat

response = chat(
    model='edtorre/qwen3.5-hermes',
    messages=[{'role': 'user', 'content': 'Hello!'}],
)
print(response.message.content)

import ollama from 'ollama'

const response = await ollama.chat({
  model: 'edtorre/qwen3.5-hermes',
  messages: [{role: 'user', content: 'Hello!'}],
})
console.log(response.message.content)

Details

Updated 5 days ago

5 days ago

c0ce9da0f773 · 6.6GB ·

model

archqwen35

·

parameters9.65B

·

quantizationQ4_K_M

6.6GB

license

Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR US

11kB

params

{ "num_ctx": 65536, "num_predict": 8192, "presence_penalty": 1.2, "temperature": 0.7

101B

template

{{ .Prompt }}

13B

Readme

A compact, agent-tuned Qwen 3.5 model built for local agent harnesses like Hermes Agent. Based on qwen3.5:latest with Q4_K_M quantization for a lightweight 6.6 GB footprint, while
retaining strong tool-calling, vision, and thinking capabilities.

## Specs

| Parameter | Value |
|—————–|————————————|
| Base Model | qwen3.5:latest |
| Architecture | Qwen 3.5 |
| Parameters | 9.7B |
| Quantization | Q4_K_M |
| Size | 6.6 GB |
| Context Window | 65,536 (64K) |
| Max Output | 8,192 tokens |
| Temperature | 0.7 |
| Top-k / Top-p | 20 / 0.9 |
| Presence Penalty| 1.2 |

## Capabilities

✅ Tools / function calling
✅ Vision
✅ Audio
✅ Thinking

## Performance

Designed to run alongside these Ollama server settings:

                                                                                                                                                                                              
 OLLAMA_FLASH_ATTENTION=1                                                                                                                                                                     
 OLLAMA_KV_CACHE_TYPE=q8_0

At only 6.6 GB with flash attention + Q8_0 KV cache, total VRAM usage at 64K context is around ~12-13 GB — very comfortable on 16-20 GB GPUs, and even feasible on some 12 GB cards.

## Quick Start

   ollama run edtorre/qwen3.5-hermes

With Hermes Agent:

   hermes config set model.default edtorre/qwen3.5-hermes                                                                                                                                     
   hermes chat

License

Apache 2.0

A compact, agent-tuned Qwen 3.5 model built for local agent harnesses like Hermes Agent. Based on `qwen3.5:latest` with Q4_K_M quantization for a lightweight 6.6 GB footprint, while      
 retaining strong tool-calling, vision, and thinking capabilities.                                                                                                                            
                                                                                                                                                                                              
   ## Specs                                                                                                                                                                                   
                                                                                                                                                                                              
   | Parameter       | Value                              |                                                                                                                                   
   |-----------------|------------------------------------|                                                                                                                                   
   | Base Model      | qwen3.5:latest                     |                                                                                                                                   
   | Architecture    | Qwen 3.5                           |                                                                                                                                   
   | Parameters      | 9.7B                               |                                                                                                                                   
   | Quantization    | Q4_K_M                             |                                                                                                                                   
   | Size            | 6.6 GB                             |                                                                                                                                   
   | Context Window  | 65,536 (64K)                       |                                                                                                                                   
   | Max Output      | 8,192 tokens                       |                                                                                                                                   
   | Temperature     | 0.7                                |                                                                                                                                   
   | Top-k / Top-p   | 20 / 0.9                           |                                                                                                                                   
   | Presence Penalty| 1.2                                |                                                                                                                                   
                                                                                                                                                                                              
   ## Capabilities                                                                                                                                                                            
                                                                                                                                                                                              
   - ✅ Tools / function calling                                                                                                                                                              
   - ✅ Vision                                                                                                                                                                                
   - ✅ Audio                                                                                                                                                                                 
   - ✅ Thinking                                                                                                                                                                              
                                                                                                                                                                                              
   ## Performance                                                                                                                                                                             
                                                                                                                                                                                              
   Designed to run alongside these Ollama server settings:                                                                                                                                    
                                                                                                                                                                                              
 ```                                                                                                                                                                                          
                                                                                                                                                                                              
 OLLAMA_FLASH_ATTENTION=1                                                                                                                                                                     
 OLLAMA_KV_CACHE_TYPE=q8_0                                                                                                                                                                    
                                                                                                                                                                                              
 ```                                                                                                                                                                                          
                                                                                                                                                                                              
   At only 6.6 GB with flash attention + Q8_0 KV cache, total VRAM usage at 64K context is around ~12-13 GB — very comfortable on 16-20 GB GPUs, and even feasible on some 12 GB cards.       
                                                                                                                                                                                              
   ## Quick Start                                                                                                                                                                             
                                                                                                                                                                                              
   ```bash                                                                                                                                                                                    
   ollama run edtorre/qwen3.5-hermes                                                                                                                                                          
 ```                                                                                                                                                                                          
                                                                                                                                                                                              
 With Hermes Agent:                                                                                                                                                                           
                                                                                                                                                                                              
 ```bash                                                                                                                                                                                      
   hermes config set model.default edtorre/qwen3.5-hermes                                                                                                                                     
   hermes chat                                                                                                                                                                                
 ```                                                                                                                                                                                          
                                                                                                                                                                                              
 License                                                                                                                                                                                      
                                                                                                                                                                                              
 Apache 2.0

![qwen3.5-hermes.jpeg](/assets/edtorre/qwen3.5-hermes/17460bbb-1a46-432e-b15b-c4cf5110fba9)

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