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Model: moutons/Llama-3.1-Swallow-JP-EN-Translator-v1-8B-mlx-fp16 Source: Original Platform
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README.md
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README.md
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---
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base_model: mpasila/Llama-3.1-Swallow-JP-EN-Translator-v1-8B
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- llama
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- mlx
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- mlx-my-repo
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license:
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- llama3.3
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- gemma
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language:
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- en
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- ja
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datasets:
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- mpasila/ParallelFiction-Ja_En-1k-16k-Gemma-3-ShareGPT-Filtered
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- NilanE/ParallelFiction-Ja_En-100k
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---
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# moutons/Llama-3.1-Swallow-JP-EN-Translator-v1-8B-mlx-fp16
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The Model [moutons/Llama-3.1-Swallow-JP-EN-Translator-v1-8B-mlx-fp16](https://huggingface.co/moutons/Llama-3.1-Swallow-JP-EN-Translator-v1-8B-mlx-fp16) was converted to MLX format from [mpasila/Llama-3.1-Swallow-JP-EN-Translator-v1-8B](https://huggingface.co/mpasila/Llama-3.1-Swallow-JP-EN-Translator-v1-8B) using mlx-lm version **0.31.2**.
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## Use with mlx
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```bash
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pip install mlx-lm
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```
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```python
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from mlx_lm import load, generate
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model, tokenizer = load("moutons/Llama-3.1-Swallow-JP-EN-Translator-v1-8B-mlx-fp16")
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prompt="hello"
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if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
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messages = [{"role": "user", "content": prompt}]
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prompt = tokenizer.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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response = generate(model, tokenizer, prompt=prompt, verbose=True)
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```
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chat_template.jinja
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{% for message in messages %}{% if message['role'] == 'user' %}{{'<|im_start|>user
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' + message['content'] + '<|im_end|>
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'}}{% elif message['role'] == 'assistant' %}{{'<|im_start|>assistant
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' + message['content'] + '<|im_end|>
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config.json
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 4096,
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Normal file
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||||
"tokenizer_class": "TokenizersBackend",
|
||||
"unk_token": null
|
||||
}
|
||||
Reference in New Issue
Block a user