Instructions to use tencent/Hy-MT2-1.8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tencent/Hy-MT2-1.8B with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="tencent/Hy-MT2-1.8B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("tencent/Hy-MT2-1.8B") model = AutoModelForCausalLM.from_pretrained("tencent/Hy-MT2-1.8B") - Notebooks
- Google Colab
- Kaggle
Update README_CN.md
Browse files- README_CN.md +30 -70
README_CN.md
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@@ -82,6 +82,33 @@ Hy-MT2 是一款面向真实复杂场景的“快思考”多语言翻译模型
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---
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## 推理和部署
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### transformers
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transformers>=5.6.0
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_path = "tencent/Hy-MT2-
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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Start the vLLM server:
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```bash
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vllm serve tencent/Hy-MT2-
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```
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### sglang
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Launch SGLang server:
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```bash
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python3 -m sglang.launch_server --model tencent/Hy-MT2-
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```
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### llama_cpp
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**❕❕ This gguf depends on our STQ kernel, which is released at [PR #22836](https://github.com/ggml-org/llama.cpp/pull/22836).**
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#### Clone llama.cpp
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```bash
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git clone https://github.com/ggml-org/llama.cpp.git
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```
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#### Enter the llama.cpp folder
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```bash
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cd llama.cpp
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```
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#### Build llama.cpp
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```bash
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cmake -B build
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cmake --build build --config Release
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```
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#### Run a completion example
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```bash
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./build/bin/llama-completion \
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--model model.gguf \
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-p "Translate the following segment into Chinese, without additional explanation:Hello" \
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--jinja \
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-ngl 0 \
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-n 64 -st
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```
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#### Run the llama.cpp benchmark
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```bash
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./build/bin/llama-bench -m model_zoo/model.gguf -ngl 0
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```
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对于1.8B和7B,我们推荐使用下面这组参数进行推理。注意,我们的模型没有默认 system_prompt。
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```json
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{
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"temperature": 0.7,
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"top_p": 0.6,
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"top_k": 20,
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"repetition_penalty": 1.05,
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"max_tokens": 4096
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}
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```
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对于30B-A3B,我们推荐使用下面这组参数进行推理。注意,我们的模型没有默认 system_prompt。
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```json
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{
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"temperature": 0.7,
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"top_p": 1.0,
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"top_k": -1,
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"repetition_penalty": 1.0,
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"max_tokens": 4096
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}
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```
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---
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## 推理和部署
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+
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对于1.8B和7B,我们推荐使用下面这组参数进行推理。注意,我们的模型没有默认 system_prompt。
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```json
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{
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"temperature": 0.7,
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"top_p": 0.6,
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"top_k": 20,
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"repetition_penalty": 1.05,
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"max_tokens": 4096
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}
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```
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对于30B-A3B,我们推荐使用下面这组参数进行推理。注意,我们的模型没有默认 system_prompt。
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```json
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{
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"temperature": 0.7,
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"top_p": 1.0,
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"top_k": -1,
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"repetition_penalty": 1.0,
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"max_tokens": 4096
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}
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```
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### transformers
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transformers>=5.6.0
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_path = "tencent/Hy-MT2-1.8B"
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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Start the vLLM server:
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```bash
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vllm serve tencent/Hy-MT2-1.8B --tensor-parallel-size 1
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```
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### sglang
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Launch SGLang server:
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```bash
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python3 -m sglang.launch_server --model tencent/Hy-MT2-1.8B --tp 1
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```
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