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add function calling support

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2023-08-30 15:04:13 +08:00
parent f1402ce523
commit 9e80cc085c
5 changed files with 627 additions and 113 deletions
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README.md
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@@ -321,7 +321,7 @@ openai.api_key = "none"
# create a request activating streaming response
for chunk in openai.ChatCompletion.create(
model="Qwen-7B",
model="Qwen",
messages=[
{"role": "user", "content": "你好"}
],
@@ -333,7 +333,7 @@ for chunk in openai.ChatCompletion.create(
# create a request not activating streaming response
response = openai.ChatCompletion.create(
model="Qwen-7B",
model="Qwen",
messages=[
{"role": "user", "content": "你好"}
],
@@ -349,6 +349,8 @@ print(response.choices[0].message.content)
<br>
<p>
Function calling is also supported (but only when `stream=False` for the moment). See the [example usage](examples/function_call_examples.py) here.
## Deployment
It is simple to run the model on CPU, which requires your specification of device:
@@ -371,22 +373,22 @@ Then you can run the 7B chat model on 2 GPUs using the above scripts.
Qwen-7B-Chat is specifically optimized for tool usage, including API, database, models, etc., so that users can build their own Qwen-7B-based LangChain, Agent, and Code Interpreter. In our evaluation [benchmark](eval/EVALUATION.md) for assessing tool usage capabilities, we find that Qwen-7B reaches stable performance.
| Model | Tool Selection (Acc.↑) | Tool Input (Rouge-L↑) | False Positive Error↓ |
| :------------ | :-----------------------: | :----------------------: | :----------------------: |
| GPT-4 | 95% | **0.90** | 15% |
| GPT-3.5 | 85% | 0.88 | 75% |
| **Qwen-7B** | **99%** | 0.89 | **9.7%** |
| Model | Tool Selection (Acc.↑) | Tool Input (Rouge-L↑) | False Positive Error↓ |
|:-----------------| :-----------------------: | :----------------------: | :----------------------: |
| GPT-4 | 95% | **0.90** | 15% |
| GPT-3.5 | 85% | 0.88 | 75% |
| **Qwen-7B-Chat** | **99%** | 0.89 | **9.7%** |
For how to write and use prompts for ReAct Prompting, please refer to [the ReAct examples](examples/react_prompt.md). The use of tools can enable the model to better perform tasks.
Additionally, we provide experimental results to show its capabilities of playing as an agent. See [Hugging Face Agent](https://huggingface.co/docs/transformers/transformers_agents) for more information. Its performance on the run-mode benchmark provided by Hugging Face is as follows:
| Model | Tool Selection↑ | Tool Used↑ | Code↑ |
| :---------------- | :----------------: | :-----------: | :---------: |
| GPT-4 | **100** | **100** | **97.41** |
| GPT-3.5 | 95.37 | 96.30 | 87.04 |
| StarCoder-15.5B | 87.04 | 87.96 | 68.89 |
| **Qwen-7B** | 90.74 | 92.59 | 74.07 |
| Model | Tool Selection↑ | Tool Used↑ | Code↑ |
|:-----------------| :----------------: | :-----------: | :---------: |
| GPT-4 | **100** | **100** | **97.41** |
| GPT-3.5 | 95.37 | 96.30 | 87.04 |
| StarCoder-15.5B | 87.04 | 87.96 | 68.89 |
| **Qwen-7B-Chat** | 90.74 | 92.59 | 74.07 |
<br>