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Oct 31, 2025
Merged

Correct agentic demo mistral parser and commands #3750

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dkalinowski Oct 30, 2025
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Merge branch 'main' into mistral-change
dkalinowski Oct 31, 2025
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17 changes: 12 additions & 5 deletions demos/continuous_batching/agentic_ai/README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -131,7 +131,7 @@ ovms.exe --pull --model_repository_path models --source_model OpenVINO/Qwen3-8B-
:sync: Mistral-7B-Instruct-v0.3-int4-ov
```bat
ovms.exe --pull --model_repository_path models --source_model OpenVINO/Mistral-7B-Instruct-v0.3-int4-ov --task text_generation --tool_parser mistral
curl -L -o models\OpenVINO\Mistral-7B-Instruct-v0.3-int4-ov\chat_template.jinja https://raw.githubusercontent.com/vllm-project/vllm/refs/tags/v0.9.0/examples/tool_chat_template_mistral.jinja
curl -L -o models\OpenVINO\Mistral-7B-Instruct-v0.3-int4-ov\chat_template.jinja https://raw.githubusercontent.com/vllm-project/vllm/refs/tags/v0.10.1.1/examples/tool_chat_template_mistral_parallel.jinja
```
:::
:::{tab-item} Phi-4-mini-instruct-int4-ov
Expand Down Expand Up @@ -411,7 +411,7 @@ Run the agentic application:
:::{tab-item} Qwen3-8B
:sync: Qwen3-8B
```bash
python openai_agent.py --query "What is the current weather in Tokyo?" --model Qwen/Qwen3-8B --base-url http://localhost:8000/v3 --mcp-server-url http://localhost:8080/sse --mcp-server all --stream --enable-thinking
python openai_agent.py --query "What is the current weather in Tokyo?" --model Qwen/Qwen3-8B --base-url http://localhost:8000/v3 --mcp-server-url http://localhost:8080/sse --mcp-server weather --stream --enable-thinking
```
```bash
python openai_agent.py --query "List the files in folder /root" --model Qwen/Qwen3-8B --base-url http://localhost:8000/v3 --mcp-server-url http://localhost:8080/sse --mcp-server all
Expand All @@ -420,7 +420,7 @@ python openai_agent.py --query "List the files in folder /root" --model Qwen/Qwe
:::{tab-item} Qwen3-4B
:sync: Qwen3-4B
```bash
python openai_agent.py --query "What is the current weather in Tokyo?" --model Qwen/Qwen3-4B --base-url http://localhost:8000/v3 --mcp-server-url http://localhost:8080/sse --mcp-server all --stream
python openai_agent.py --query "What is the current weather in Tokyo?" --model Qwen/Qwen3-4B --base-url http://localhost:8000/v3 --mcp-server-url http://localhost:8080/sse --mcp-server weather --stream
```
```bash
python openai_agent.py --query "List the files in folder /root" --model Qwen/Qwen3-4B --base-url http://localhost:8000/v3 --mcp-server-url http://localhost:8080/sse --mcp-server all
Expand All @@ -435,13 +435,13 @@ python openai_agent.py --query "List the files in folder /root" --model meta-lla
:::{tab-item} Mistral-7B-Instruct-v0.3
:sync: Mistral-7B-Instruct-v0.3
```bash
python openai_agent.py --query "List the files in folder /root" --model mistralai/Mistral-7B-Instruct-v0.3 --base-url http://localhost:8000/v3 --mcp-server-url http://localhost:8080/sse --mcp-server weather
python openai_agent.py --query "List the files in folder /root" --model mistralai/Mistral-7B-Instruct-v0.3 --base-url http://localhost:8000/v3 --mcp-server-url http://localhost:8080/sse --mcp-server all --tool_choice required
```
:::
:::{tab-item} Llama-3.2-3B-Instruct
:sync: Llama-3.2-3B-Instruct
```bash
python openai_agent.py --query "List the files in folder /root" --model meta-llama/Llama-3.2-3B-Instruct --base-url http://localhost:8000/v3 --mcp-server-url http://localhost:8080/sse --mcp-server weather
python openai_agent.py --query "List the files in folder /root" --model meta-llama/Llama-3.2-3B-Instruct --base-url http://localhost:8000/v3 --mcp-server-url http://localhost:8080/sse --mcp-server all
```
:::
:::{tab-item} Phi-4-mini-instruct
Expand Down Expand Up @@ -537,9 +537,16 @@ input_num_tokens 50.0 2298.92 973.02 520.00 1556.50 2367.00 3100.75
Testing model accuracy is critical for a successful adoption in AI application. The recommended methodology is to use BFCL tool like describe in the [testing guide](../accuracy/README.md#running-the-tests-for-agentic-models-with-function-calls).
Here is example of the response from the OpenVINO/Qwen3-8B-int4-ov model:
```
--test-category simple
{"accuracy": 0.9525, "correct_count": 381, "total_count": 400}

--test-category multiple
{"accuracy": 0.89, "correct_count": 178, "total_count": 200}

--test-category parallel
{"accuracy": 0.89, "correct_count": 178, "total_count": 200}

--test-category irrelevance
{"accuracy": 0.825, "correct_count": 198, "total_count": 240}
```

Expand Down