[pull] main from bentoml:main

README.md

@@ -35,105 +35,79 @@ OpenLLM supports a wide range of state-of-the-art open-source LLMs. You can also
     <th>Start a Server</th>
   </tr>
   <tr>
-    <td>deepseek-r1</td>
-    <td>671B</td>
-    <td>80Gx16</td>
-    <td><code>openllm serve deepseek-r1:671b-fc3d</code></td>
-  </tr>
-  <tr>
-    <td>deepseek-r1-distill</td>
-    <td>14B</td>
-    <td>80G</td>
-    <td><code>openllm serve deepseek-r1-distill:qwen2.5-14b-98a9</code></td>
-  </tr>
-  <tr>
-    <td>deepseek-v3</td>
-    <td>671B</td>
-    <td>80Gx16</td>
-    <td><code>openllm serve deepseek-v3:671b-instruct-d7ec</code></td>
+    <td>deepseek</td>
+    <td>8B</td>
+    <td>24GB</td>
+    <td><code>openllm serve deepseek:r1-distill-llama3.1-8b-626a</code></td>
   </tr>
   <tr>
     <td>gemma2</td>
     <td>2B</td>
     <td>12G</td>
-    <td><code>openllm serve gemma2:2b-instruct-747d</code></td>
+    <td><code>openllm serve gemma2:2b-instruct-868c</code></td>
+  </tr>
+  <tr>
+    <td>hermes-3</td>
+    <td>8B</td>
+    <td>80G</td>
+    <td><code>openllm serve hermes-3:deep-llama3-8b-1242</code></td>
   </tr>
   <tr>
     <td>llama3.1</td>
     <td>8B</td>
     <td>24G</td>
-    <td><code>openllm serve llama3.1:8b-instruct-3c0c</code></td>
+    <td><code>openllm serve llama3.1:8b-instruct-a995</code></td>
   </tr>
   <tr>
     <td>llama3.2</td>
     <td>1B</td>
     <td>24G</td>
-    <td><code>openllm serve llama3.2:1b-instruct-f041</code></td>
+    <td><code>openllm serve llama3.2:1b-instruct-6fa1</code></td>
   </tr>
   <tr>
     <td>llama3.3</td>
     <td>70B</td>
     <td>80Gx2</td>
-    <td><code>openllm serve llama3.3:70b-instruct-b850</code></td>
+    <td><code>openllm serve llama3.3:70b-instruct-f791</code></td>
   </tr>
   <tr>
     <td>mistral</td>
     <td>8B</td>
     <td>24G</td>
-    <td><code>openllm serve mistral:8b-instruct-50e8</code></td>
+    <td><code>openllm serve mistral:8b-instruct-f4ed</code></td>
   </tr>
   <tr>
     <td>mistral-large</td>
     <td>123B</td>
     <td>80Gx4</td>
-    <td><code>openllm serve mistral-large:123b-instruct-1022</code></td>
-  </tr>
-  <tr>
-    <td>mistralai</td>
-    <td>24B</td>
-    <td>80G</td>
-    <td><code>openllm serve mistralai:24b-small-instruct-2501-0e69</code></td>
-  </tr>
-  <tr>
-    <td>mixtral</td>
-    <td>7B</td>
-    <td>80Gx2</td>
-    <td><code>openllm serve mixtral:8x7b-instruct-v0.1-b752</code></td>
+    <td><code>openllm serve mistral-large:123b-instruct-2407-e1ef</code></td>
   </tr>
   <tr>
     <td>phi4</td>
     <td>14B</td>
     <td>80G</td>
-    <td><code>openllm serve phi4:14b-c12d</code></td>
+    <td><code>openllm serve phi4:14b-a515</code></td>
   </tr>
   <tr>
     <td>pixtral</td>
     <td>12B</td>
     <td>80G</td>
-    <td><code>openllm serve pixtral:12b-240910-c344</code></td>
+    <td><code>openllm serve pixtral:12b-2409-a2e0</code></td>
   </tr>
   <tr>
     <td>qwen2.5</td>
     <td>7B</td>
     <td>24G</td>
-    <td><code>openllm serve qwen2.5:7b-instruct-3260</code></td>
+    <td><code>openllm serve qwen2.5:7b-instruct-dbe1</code></td>
   </tr>
   <tr>
     <td>qwen2.5-coder</td>
-    <td>7B</td>
-    <td>24G</td>
-    <td><code>openllm serve qwen2.5-coder:7b-instruct-e75d</code></td>
-  </tr>
-  <tr>
-    <td>qwen2.5vl</td>
     <td>3B</td>
     <td>24G</td>
-    <td><code>openllm serve qwen2.5vl:3b-instruct-4686</code></td>
+    <td><code>openllm serve qwen2.5-coder:3b-instruct-63b0</code></td>
   </tr>
 </table>
 
-...
-
 For the full model list, see the [OpenLLM models repository](https://github.com/bentoml/openllm-models).
 
 ## Start an LLM server
@@ -151,7 +125,7 @@ To start an LLM server locally, use the `openllm serve` command and specify the
 >    ```
 
 ```bash
-openllm serve openllm serve llama3.2:1b-instruct-f041
+openllm serve openllm serve llama3.2:1b-instruct-6fa1
 ```
 
 The server will be accessible at [http://localhost:3000](http://localhost:3000/), providing OpenAI-compatible APIs for interaction. You can call the endpoints with different frameworks and tools that support OpenAI-compatible APIs. Typically, you may need to specify the following:
@@ -235,7 +209,7 @@ openllm repo update
 To review a model’s information, run:
 
 ```bash
-openllm model get openllm serve llama3.2:1b-instruct-f041
+openllm model get openllm serve llama3.2:1b-instruct-6fa1
 ```
 
 ### Add a model to the default model repository
@@ -263,7 +237,7 @@ OpenLLM supports LLM cloud deployment via BentoML, the unified model serving fra
 [Sign up for BentoCloud](https://www.bentoml.com/) for free and [log in](https://docs.bentoml.com/en/latest/bentocloud/how-tos/manage-access-token.html). Then, run `openllm deploy` to deploy a model to BentoCloud:
 
 ```bash
-openllm deploy openllm serve llama3.2:1b-instruct-f041
+openllm deploy openllm serve llama3.2:1b-instruct-6fa1
 ```
 
 > [!NOTE]
@@ -296,3 +270,4 @@ This project uses the following open-source projects:
 - [astral-sh/uv](https://github.com/astral-sh/uv) for blazing fast model requirements installing
 
 We are grateful to the developers and contributors of these projects for their hard work and dedication.
+

README.md.tpl

@@ -44,7 +44,6 @@ OpenLLM supports a wide range of state-of-the-art open-source LLMs. You can also
   {%- endfor %}
 </table>
 
-...
 
 For the full model list, see the [OpenLLM models repository](https://github.com/bentoml/openllm-models).
 

gen_readme.py

@@ -5,18 +5,6 @@
 #     "uv",
 # ]
 # ///
+import subprocess, sys, pathlib, json, jinja2
 
-import subprocess, sys, pathlib, json
-
-from jinja2 import Environment, FileSystemLoader
-
-wd = pathlib.Path('.').parent
-model_dict = subprocess.run(
-    [sys.executable, '-m', 'uv', 'run', '--with-editable', '.', 'openllm', 'model', 'list', '--output', 'readme'],
-    capture_output=True,
-    text=True,
-    check=True,
-)
-E = Environment(loader=FileSystemLoader('.'))
-with (wd / 'README.md').open('w') as f:
-    f.write(E.get_template('README.md.tpl').render(model_dict=json.loads(model_dict.stdout.strip())))
+with (pathlib.Path('.').parent / 'README.md').open('w') as f: f.write(jinja2.Environment(loader=jinja2.FileSystemLoader('.')).get_template('README.md.tpl').render(model_dict=json.loads(subprocess.run([sys.executable, '-m', 'uv', 'run', '--with-editable', '.', 'openllm', 'model', 'list', '--output', 'readme'], text=True, check=True, capture_output=True).stdout.strip())))