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--- |
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license: apache-2.0 |
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datasets: |
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- teknium/OpenHermes-2.5 |
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language: |
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- en |
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--- |
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## Training |
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- 8x A6000s |
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- [Forked version of unsloth](https://github.com/serp-ai/unsloth) for efficient training |
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- Sequence Length: 4096 |
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- Effective batch size: 128 |
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- Learning Rate: 2e-5 with linear decay |
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- Epochs: 1 |
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- [Base model](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) trained with QLoRA (rank 64, alpha 16) and MoE adapters/routers trained in bf16 |
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- Num Experts: 16 |
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- Top K: 4 |
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- Adapter Dim: 512 |
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## Prompt Format |
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``` |
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<|im_start|>system\n{message}<|im_end|>\n<|im_start|>user\n{message}<|im_end|>\n<|im_start|>assistant\n |
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``` |
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## Usage |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("serpdotai/sparsetral-16x7B-v2", trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained("serpdotai/sparsetral-16x7B-v2", device_map="auto", trust_remote_code=True).eval() |
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system_str = "<|im_start|>system\n{message}<|im_end|>\n" |
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user_str = "<|im_start|>user\n{message}<|im_end|>\n" |
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assistant_str = "<|im_start|>assistant\n{message}<|im_end|>\n" |
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def construct_prompt(messages): |
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prompt = "" |
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for message in messages: |
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if message["from"] in ["human", "user"]: |
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prompt += user_str.format( |
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message=message["value"] |
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) |
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elif message["from"] in ["gpt", "assistant"]: |
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prompt += assistant_str.format( |
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message=message["value"] |
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) |
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elif message["from"] in ["system", "instruction"]: |
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prompt += system_str.format( |
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message=message["value"] |
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) |
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else: |
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raise ValueError( |
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f"Unknown message type: {message['from']}" |
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) |
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return prompt + "<|im_start|>assistant\n" |
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system = "You are a helpful assistant who will help the user to the best of their ability. If you don't know something, say \"I don't know\"" |
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user = "Are you sentient?" |
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messages = [ |
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{"from": "system", "value": system}, |
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{"from": "user", "value": user}, |
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] |
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prompt = construct_prompt(messages) |
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inputs = tokenizer(prompt, return_tensors="pt") |
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inputs = inputs.to(model.device) |
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pred = model.generate(**inputs, max_length=4096, do_sample=True, top_k=50, top_p=0.99, temperature=0.9, num_return_sequences=1) |
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print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True)) |
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``` |
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## Other Information |
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Paper reference: [Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks](https://arxiv.org/abs/2401.02731) |
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[Original Paper repo](https://github.com/wuhy68/Parameter-Efficient-MoE) |
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[Forked repo with mistral support (sparsetral)](https://github.com/serp-ai/Parameter-Efficient-MoE) |
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If you are interested in faster inferencing, check out our [fork of vLLM](https://github.com/serp-ai/vllm) that adds sparsetral support |
