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openchat_v3.2

Text Generation
Transformers
PyTorch
llama
text-generation-inference
Inference Endpoints
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[doc] Update README

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@@ -33,35 +33,23 @@ To deploy the server as an online service, use `--api-keys sk-KEY1 sk-KEY2 ...`
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  curl http://localhost:18888/v1/chat/completions \
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  -H "Content-Type: application/json" \
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  -d '{
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- "model": "openchat_v3.1_llama2",
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  "messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}]
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  }'
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  ```
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  </details>
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- | Model | Size | Context | Weights | Serving |
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- |---------------|------|---------|-------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------|
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- | OpenChat 3.1 | 13B | 4096 | [Huggingface](https://huggingface.co/openchat/openchat_v3.1) | `python -m ochat.serving.openai_api_server --model-type openchat_v3.1_llama2 --model openchat/openchat_v3.1 --engine-use-ray --worker-use-ray --max-num-batched-tokens 5120` |
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- | OpenChat 3.2 | 13B | 4096 | [Huggingface](https://huggingface.co/openchat/openchat_v3.2) | `python -m ochat.serving.openai_api_server --model-type openchat_v3.2 --model openchat/openchat_v3.2 --engine-use-ray --worker-use-ray --max-num-batched-tokens 5120` |
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  For inference with Huggingface Transformers (slow and not recommended), follow the conversation template provided below:
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  <details>
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  <summary>Conversation templates (click to expand)</summary>
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- V3.1
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-
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- ```python
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- # Single-turn V3.1
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- tokenize("Assistant is GPT4<|end_of_turn|>User: Hello<|end_of_turn|>Assistant:")
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- # Result: [1, 4007, 22137, 338, 402, 7982, 29946, 32000, 4911, 29901, 15043, 32000, 4007, 22137, 29901]
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-
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- # Multi-turn V3.1
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- tokenize("Assistant is GPT4<|end_of_turn|>User: Hello<|end_of_turn|>Assistant: Hi<|end_of_turn|>User: How are you today?<|end_of_turn|>Assistant:")
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- # Result: [1, 4007, 22137, 338, 402, 7982, 29946, 32000, 4911, 29901, 15043, 32000, 4007, 22137, 29901, 6324, 32000, 4911, 29901, 1128, 526, 366, 9826, 29973, 32000, 4007, 22137, 29901]
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- ```
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-
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  V3.2
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  ```python
@@ -74,6 +62,18 @@ tokenize("GPT4 User: Hello<|end_of_turn|>GPT4 Assistant: Hi<|end_of_turn|>GPT4 U
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  # Result: [1, 402, 7982, 29946, 4911, 29901, 15043, 32000, 402, 7982, 29946, 4007, 22137, 29901, 6324, 32000, 402, 7982, 29946, 4911, 29901, 1128, 526, 366, 9826, 29973, 32000, 402, 7982, 29946, 4007, 22137, 29901]
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  ```
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  </details>
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  ## <a id="benchmarks"></a> Benchmarks
@@ -82,16 +82,16 @@ We have evaluated our models using the two most popular evaluation benchmarks **
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  To ensure consistency, we used the same routine as ChatGPT / GPT-4 to run these benchmarks. We started the OpenAI API-compatible server and set the `openai.api_base` to `http://localhost:18888/v1` in the benchmark program.
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- | **Model** | **Size** | **Context** | **💲Free** | **AlpacaEval (win rate %)** | **MT-bench (score)** | **MT-bench (win rate adjusted %)** |
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- |------------------|----------|-------------|-----------|-----------------------------|----------------------|------------------------------------|
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- | | | | | **v.s. text-davinci-003** | | **v.s. ChatGPT** |
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- | GPT-4 | 1.8T* | 8K | �� | 95.3 | 8.99 | 82.5 |
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- | ChatGPT | 175B* | 4K | ❌ | 89.4 | 7.94 | 50.0 |
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- | Llama-2-70B-Chat | 70B | 4K | ✅ | 92.7 | 6.86 | |
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- | **OpenChat 3.1** | 13B | 4K | ✅ | **89.5** | **6.65** | **50.0** |
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- | **OpenChat 3.2** | 13B | 4K | ✅ | **89.1** | **7.01** | **51.6** |
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- | Llama-2-13B-Chat | 13B | 4K | ✅ | 81.0 | 6.65 | |
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- | Vicuna 1.3 | 13B | 2K | ❌ | 82.1 | 6.00 | 37.5 |
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  *: Estimated model size
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  curl http://localhost:18888/v1/chat/completions \
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  -H "Content-Type: application/json" \
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  -d '{
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+ "model": "openchat_v3.2",
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  "messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}]
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  }'
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  ```
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  </details>
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+ | Model | Size | Context | Weights | Serving |
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+ |--------------|------|---------|--------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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+ | OpenChat 3.2 | 13B | 4096 | [Huggingface](https://huggingface.co/openchat/openchat_v3.2) | `python -m ochat.serving.openai_api_server --model-type openchat_v3.2 --model openchat/openchat_v3.2 --engine-use-ray --worker-use-ray --max-num-batched-tokens 5120` |
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+ | OpenChat 3.1 | 13B | 4096 | [Huggingface](https://huggingface.co/openchat/openchat_v3.1) | `python -m ochat.serving.openai_api_server --model-type openchat_v3.1_llama2 --model openchat/openchat_v3.1 --engine-use-ray --worker-use-ray --max-num-batched-tokens 5120` |
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  For inference with Huggingface Transformers (slow and not recommended), follow the conversation template provided below:
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  <details>
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  <summary>Conversation templates (click to expand)</summary>
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  V3.2
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  ```python
 
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  # Result: [1, 402, 7982, 29946, 4911, 29901, 15043, 32000, 402, 7982, 29946, 4007, 22137, 29901, 6324, 32000, 402, 7982, 29946, 4911, 29901, 1128, 526, 366, 9826, 29973, 32000, 402, 7982, 29946, 4007, 22137, 29901]
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  ```
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+ V3.1
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+
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+ ```python
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+ # Single-turn V3.1
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+ tokenize("Assistant is GPT4<|end_of_turn|>User: Hello<|end_of_turn|>Assistant:")
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+ # Result: [1, 4007, 22137, 338, 402, 7982, 29946, 32000, 4911, 29901, 15043, 32000, 4007, 22137, 29901]
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+
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+ # Multi-turn V3.1
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+ tokenize("Assistant is GPT4<|end_of_turn|>User: Hello<|end_of_turn|>Assistant: Hi<|end_of_turn|>User: How are you today?<|end_of_turn|>Assistant:")
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+ # Result: [1, 4007, 22137, 338, 402, 7982, 29946, 32000, 4911, 29901, 15043, 32000, 4007, 22137, 29901, 6324, 32000, 4911, 29901, 1128, 526, 366, 9826, 29973, 32000, 4007, 22137, 29901]
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+ ```
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+
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  </details>
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  ## <a id="benchmarks"></a> Benchmarks
 
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  To ensure consistency, we used the same routine as ChatGPT / GPT-4 to run these benchmarks. We started the OpenAI API-compatible server and set the `openai.api_base` to `http://localhost:18888/v1` in the benchmark program.
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+ | **Model** | **Size** | **Context** | **💲Free** | **AlpacaEval (win rate %)** | **MT-bench (win rate adjusted %)** | **MT-bench (score)** |
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+ |------------------|----------|-------------|------------|-----------------------------|------------------------------------|----------------------|
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+ | | | | | **v.s. text-davinci-003** | **v.s. ChatGPT** | |
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+ | GPT-4 | 1.8T* | 8K | ❌ | 95.3 | 82.5 | 8.99 |
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+ | ChatGPT | 175B* | 4K | ❌ | 89.4 | 50.0 | 7.94 |
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+ | Llama-2-70B-Chat | 70B | 4K | ✅ | 92.7 | | 6.86 |
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+ | **OpenChat 3.2** | **13B** | **4K** | ✅ | **89.1** | **51.6** | **7.01** |
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+ | **OpenChat 3.1** | **13B** | **4K** | ✅ | **89.5** | **50.0** | **6.65** |
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+ | Llama-2-13B-Chat | 13B | 4K | ✅ | 81.0 | | 6.65 |
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+ | Vicuna 1.3 | 13B | 2K | ❌ | 82.1 | 37.5 | 6.00 |
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  *: Estimated model size
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